Power Regression Model Research Articles

Relação entre o peso corporal e a circunferência escrotal em ovinos Pelibuey em crescimento criados em condições tropicais

ABSTRACT The body weight (BW) of an animal is a vital economic trait that might help in decision-making in the handling of animals. The objective of the present study was to develop equations for the prediction of BW in Pelibuey sheep using scrotal circumference (SC). The BW (23.40 ± 6.96 kg) and SC (20.25 ± 6.19 cm) have been recorded in 405 male Pelibuey at the Southeastern Center for Ovine Integration, Mexico. Linear, logarithmic, quadratic, exponential, cubic, and power regression models were used for data analysis. Pearson correlation (R), Coefficient of determination (R2), Adjusted coefficient of determination (Adj.R2) Root mean square error (RMSE), Akaike information criterion (AIC), and Bayesian information criterion (BIC) were used to select the best model. Power regression model showed the highest R (0.93), R2 (0.86), Adj.R2 (0.86) and lowest RMSE (0.02), AIC (-989.44) and BIC (-981.44). The current study suggests that SC might be used as the only predictor for BW of growing Pelibuey sheep raised under tropical conditions.

Estimation of Aboveground Carbon Stocks in Forests Based on LiDAR and Multispectral Images: A Case Study of Duraer Coniferous Forests

The correct estimation of forest aboveground carbon stocks (AGCs) allows for an accurate assessment of the carbon sequestration potential of forest ecosystems, which is important for in-depth studies of the regional ecological environment and global climate change. How to estimate forest AGCs quickly and accurately and realize dynamic monitoring has been a hot topic of research in the forestry field worldwide. LiDAR and remote sensing optical imagery can be used to monitor forest resources, enabling the simultaneous acquisition of forest structural properties and spectral information. A high-density LiDAR-based point cloud cannot only reveal stand-scale forest parameters but can also be used to extract single wood-scale forest parameters. However, there are multiple forest parameter estimation model problems, so it is especially important to choose appropriate variables and models to estimate forest AGCs. In this study, we used a Duraer coniferous forest as the study area and combined LiDAR, multispectral images, and measured data to establish multiple linear regression models and multiple power regression models to estimate forest AGCs. We selected the best model for accuracy evaluation and mapped the spatial distribution of AGC density. We found that (1) the highest accuracy of the multiple multiplicative power regression model was obtained for the estimated AGC (R2 = 0.903, RMSE = 10.91 Pg) based on the LiDAR-estimated DBH; the predicted AGC values were in the range of 4.1–279.12 kg C. (2) The highest accuracy of the multiple multiplicative power regression model was obtained by combining the normalized vegetation index (NDVI) with the predicted AGC based on the DBH estimated by LiDAR (R2 = 0.906, RMSE = 10.87 Pg); the predicted AGC values were in the range of 3.93–449.07 kg C. (3) The LiDAR-predicted AGC values and the combined LiDAR and optical image-predicted AGC values agreed with the field AGCs.

Modelling of Speech Parameters of Punjabi by Pre-trained Deep Neural Network Using Stacked Denoising Autoencoders

Statistical parametric speech synthesis techniques such as deep neural network (DNN) and hidden Markov model (HMM) have grown in popularity since last decade over the concatenative speech synthesis approaches by modelling excitation and spectral parameters of speech to synthesize the waveforms from the written text. Due to inappropriate acoustic modelling, speech synthesized using HMM-based speech synthesis sounds muffled. DNN tried to improve the acoustic model by replacing decision trees in HMM with powerful regression model. Further, the performance of a deep neural network is greatly enhanced using pre-learning either restricted Boltzmann machines (RBM) or autoencoders. RBMs are capable to map multi-modal property of speech but result in spectral distortion of synthesized speech waveforms as non-consideration of reconstruction error. This article proposed the model of deep neural network, which is pre-trained using stacked denoising autoencoders to map speech parameters of the Punjabi language. Denoising autoencoders work by adding noise in the training data and then reconstructing the original measurements to reduce the reconstruction error. The synthesized voice using the proposed model showed the VARN of 0.82, F0 RMSE (Hz) 9.03, and V/UV error rate of 4.04% have been observed.

Bayesian Exploration of Phenomenological EoS of Neutron/Hybrid Stars with Recent Observations

The description of the stellar interior of compact stars remains as a big challenge for the nuclear astrophysics community. The consolidated knowledge is restricted to density regions around the saturation of hadronic matter ρ0=2.8×1014gcm−3, regimes where our nuclear models are successfully applied. As one moves towards higher densities and extreme conditions up to the quark/gluons deconfinement, little can be said about the microphysics of the equation of state (EoS). Here, we employ a Markov Chain Monte Carlo (MCMC) strategy to access the variability at high density regions of polytropic piecewise models for neutron star (NS) EoS or possible hybrid stars, i.e., a NS with a small quark-matter core. With a fixed description of the hadronic matter for low density, below the nuclear saturation density, we explore a variety of models for the high density regimes leading to stellar masses near to 2.5M⊙, in accordance with the observations of massive pulsars. The models are constrained, including the observation of the merger of neutrons stars from VIRGO-LIGO and with the pulsar observed by NICER. In addition, we also discuss the possibility of the use of a Bayesian power regression model with heteroscedastic error. The set of EoS from the Laser Interferometer Gravitational-Wave Observatory (LIGO) was used as input and treated as the data set for the testing case.

Predicting Perioperative Mortality in Children: Academic Endeavor or Clinical Value?

Predicting Perioperative Mortality in Children: Academic Endeavor or Clinical Value?

VOLUME TABLE OF Acacia auriculiformis A. CUNN. EX BENTH. AND Acacia mangium WILLD. PLANTED IN WOODLOT OF SOCIAL FORESTRY PROJECT IN DHAKA FOREST DIVISION OF BANGLADESH

Tree volume, the traditional measurement of wood quantity is a function of trees’ height, basal area, shape, and bark thickness. Tree volume table is the statement of expected tree volume of nominated dimensions in a particular forest stand or population. Acacia auriculiformis A. Cunn. ex. Benth. and Acacia mangium Willd. are two fast growing and hardy leguminous (Leguminosae, Mimosaceae) exotic species have widely been planted in the woodlot of Asian Development Bank financed Social Forestry Project in Bangladesh. In this study, models for calculating tree stem volume and a two way volume tables for both A. auriculiformis and A. mangium were developed. Data were collected from the social forestry woodlot of the respective species in Dhaka Forest Division, Bangladesh. Simple linear, logarithmic, polynomial, power and exponential regression models were developed. Best-fit model was selected based on highest R2 among seven linear regression models for volume calculation of A. auriculiformis and A. mangium. Regression R2 of the simple linear regression model for both the species was either higher or at least equal in compararison with said logarithmic, polynomial, power, and exponential regression models. Therefore, simple linear regression model as a function of height (h) and diameter at breast height (dbh) were adopted for the preparation of two way local volume table for both the species due to its simplicity in application.

Estimativa de área foliar de Congea tomentosa através de método não destrutivo

ABSTRACT Congea tomentosa is a climbing plant suitable for covering arbors, railings, and fences. Leaf area determination is useful in understanding the plant-environment relationship and facilitating agronomic studies on transpiration, water requirement, light interception, and photosynthetic activity. The objective of this study was to obtain an allometric equation to estimate the leaf area of C. tomentosa by measuring the leaf dimensions. Analyses were performed on 200 leaves of different shapes and sizes from 10 randomly chosen adult plants grown under field conditions. The leaf length, leaf width, product length and width, and leaf area were determined. Linear, linear without intercept, quadratic, cubic, power, and exponential regression models were used to estimate the leaf area. The coefficient of determination, Willmott’s concordance index, Akaike information criterion, root mean square error and BIAS index were used to determine the best model. The leaf area of C. tomentosa can be satisfactorily estimated using a non-destructive method that uses measurements of leaf dimensions. The equation ŷ = 0.63 × LW (Leaf: L = length, W = width) estimates the leaf area of C. tomentosa in a practical and fast way, with 99.15% of precision. Estimation of the leaf area of C. tomentosa using statistical models is less expensive and easily accessible to researchers and producers of this plant.

Experimental and numerical investigation on stress concentration at brace-ring intersection (BRI) of internally ring stiffened tubular T-joints

In oil and gas industry, jacket structures are commonly used in shallow to moderate water depth to support drilling and production facilities. The jacket structures, especially joints are subjected to cyclic loads from wind and waves and require special attention for fatigue induced cracks. Thickened cans are normally used in such locations to reduce the local deformation and associated hot spot stresses, and in some cases use of internal ring stiffeners have been employed as a substitute to limit the large thickness at joints. Performance of such ring stiffeners at the joints against local deformation has been established and empirical equations to determine the stress concentration factor (SCF) at the brace-chord intersection has been in use developed by Lloyds Register. However, the SCF at the Brace Ring Intersection (BRI) has not been well established, leading to failure of joint. Hence a comprehensive study involving large number of linear finite element simulations for varying parameters of chord, brace and rings have been carried out to obtain database of SCFs at BRI location and validated by experiments. Parametric study has been carried out considering slenderness ratio (γ), diameter ratio (β), thickness ratio (τ), depth ratio (η), and thickness ratio of ring (τr). Set of empirical equations have been developed using power regression model to calculate the SCFs at BRI locations in addition to brace-chord intersection.

An Improved Hybrid Model for Nonlinear Regression with Missing Values Using Deep Quasi‐Linear Kernel

AbstractMissing values are ubiquitous in the nonlinear regression research, and may lead to bias and a loss of efficiency. Even in a large dataset, values drop‐out can substantially reduce the available information for analysis. In this paper, we propose an improved hybrid model to solve the nonlinear regression problem under missing data scenarios, consisting of two parts: an overcomplete winner‐take‐all (WTA) autoencoder and a multilayer gated linear network. The WTA autoencoder is trained in an adversarial training process by taking advantage of gradually renewed teacher signals and the discrimination of missing values and observed values, and is designed to play two roles: (1) to impute missing components conditioned on observed samples; (2) to generate gate control sequences. On the other hand, the multilayer gated linear network with the generated gate control sequences implements a powerful piecewise linear regression model, whose parameters are optimized by formulating a support vector regression (SVR) with a deep quasi‐linear kernel. Experimental results based on different real‐world datasets demonstrate the effectiveness of our proposed hybrid model. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Experimental and Statistical Evaluation of the Mechanical Performance of (Jute and Cocopeat) Plant and (Silk) Animal-based Hybrid Fibers Reinforced with Epoxy Polymers

ABSTRACT In India, research on the development of new composite materials is extensively increased. In the present study, composites are developed with the renewable materials of plant and animal-based natural fibers. The present study will lead to the experimental and statistical investigation of the composite produced with a combination of plant-based natural fibers, i.e., jute and coco peat powder with the animal-based natural fibers, i.e., silk. In the present study, jute and silk were utilized as reinforcement material, cocopeat powder as a filler material (5%), and epoxy resin as matrix material (35%). The composites were prepared with the varying composition of reinforcement material, and also the filler and matrix material were kept constant. Furthermore, mechanical properties such as tensile, flexural, and impact tests were performed on the developed composites. Further, a comparative study was drawn on the mechanical test results of tensile and flexural strength of the composite using power and polynomial regression model. The regression coefficient (R2) was used to study the correlation between the experimental and predicted values. The results showed that the polynomial regression is the best suitable mathematical model than the power regression model for predicting the composites’ tensile and flexural test performance.

Inland ship emission inventory and its impact on air quality over the middle Yangtze River, China

Shipping emissions have been considered a significant source of air pollution in the cities along the Yangtze River, with severe impacts on the climate and human health. This study created a complete annual ship emission inventory for the middle reaches of the Yangtze River and assessed its impact on air quality on a regional scale. To estimate the complete emissions, 9 main engine power regression models for different ship types were created to handle those vessels with absent main power data, and a high spatial-temporal resolution annual emission inventory was developed with the activity-based method combined with Automatic Identification System (AIS) data of the full year of 2018. The total emissions of CO2, CO, SO2, NOX, PM2.5, PM10 and HC in middle reaches of the Yangtze River were 5.67 × 105, 1.02 × 103, 5.41 × 102, 1.06 × 104, 2.43 × 102, 2.45 × 102 and 3.52 × 102 tons respectively. Then, the Weather Research and Forecasting with Chemistry (WRF-Chem) model was used to study the dispersion of the ship pollutants in the atmosphere and quantize the impact on the urban area. This research will provide services for the maritime authorities to develop green shipping and emission supervision.

New distribution record of Varuna litterata from Caraga Region, Philippines: Analysis on morphometry, length/width-weight relationship, and condition factor

Abstract. Jumawan JH, Ruales JJJ, Avila MCA. 2022. New distribution record of Varuna litterata from Caraga Region, Philippines: Analysis on morphometry, length/width-weight relationship, and condition factor. Biodiversitas 23: 2935-2942. The study reported the occurrence of Varuna litterata in its habitat as a new record in Caraga Region, Philippines. The crab samples were collected using a hand-picked method and were of marketable sizes. There were 14 morphometric traits measured with the corresponding weight and sex of each specimen. Majority of the crab samples were medium-sized, with weight ranging from 11-20 grams, a carapace width range of 28-34 cm, and a carapace length range of 27-32 cm. Exploratory analysis of length /width-weight data fits the power regression model depicted into graphs. A strong relationship and highly significant was observed on pooled samples of WT-CW variables (R2: 0.9229: P<0.001) and WT-CL variables (R2: 0.9197); P<0.001). The derived log-transformed for WT-CW relationship was logWT: -2.7994+2.6456 logCW. Results suggest that carapace length and width tend to be smaller relative to the weight, which is an indication of a negative allometric growth pattern. The computed Fulton's condition factor was higher in males than females, as supported by Kruskal Wallis test (P<0.001). The data provides baseline information for comparing similar studies and in understanding the population dynamics of this crab species.

Estimating leaf area of basil cultivars through linear dimensions of leaves

Ocimum basilicum L. (basil) is an annual herb belonging to the Lamiaceae family that has economic importance for many regions around the world. Thus, ecophysiological studies are needed to assess this species growth and dispersal. This work aimed to obtain equations from regression models that meaningfully estimate the leaf area of ​​basil cultivars using linear dimensions of leaves. For this purpose, 300 leaves from 'Italiano Roxo' and 500 leaves from 'Folha Fina' cultivar were collected from plants cultivated in a greenhouse. Then, the length, width, and leaf area of each leaf were measured, and product of length by width were calculated. The equations were adjusted using the simple linear, linear without intercept, quadratic, cubic, power, and exponential regression models. Criteria for selecting the best equations were highest determination coefficient and Willmott's agreement index, lowest Akaike information criterion and root mean square error, and BIAS index closest to zero. All the equations fitted using the product of length by width (L.W) can estimate the leaf area of basil cultivars. Thus, basil leaf area can be estimated through a non-destructive method using linear dimensions of leaves. However, the equation ŷ = 0.8175*LW0.9307 is the most suitable for 'Italiano Roxo' and ŷ = 0.6335*LW for 'Folha Fina'.

Z-score regression model for coronary artery diameter in healthy Chinese Han children.

Coronary artery distension and aneurysm are complications of Kawasaki disease in children. To develop a Z-score regression model for coronary artery diameter in children that could be used as reference. This retrospective analysis included children with normal heart structure between March 2013 and April 2017. Body surface area (BSA) was calculated. The diameters of the right coronary, left main coronary, left anterior descending, and circumflex arteries were measured by echocardiography. Pearson correlation analysis was used to establish linear, exponential, logarithmic, power, and square root regression models. The analysis included 509 children (280 boys) aged 1 day to 15.2 years. Coronary artery diameters were significantly correlated with age, height, body mass, BSA, and (r = 0.663-0.826; P < 0.05), with a stronger correlation for than BSA (P < 0.05). The adjusted determination coefficients () were higher for the exponential and square root models than for the other models (P < 0.05). The random error term variance was constant for the exponential model (P > 0.05), and processing with the weighted least-square methods eliminated heteroscedasticity in the other models. The Z-scores were normally distributed for the exponential and square root models (P > 0.05). Overall, the square root model was the optimal equation for the calculation of coronary artery Z-score in Chinese Han children. This model could be used to facilitate the diagnosis of coronary artery distension in children with suspected Kawasaki disease.

Analysis of Deaths and Favorable Developments of Patients with SARS-CoV-2 Hospitalized in the Largest Hospital for Infectious Diseases and Pneumo-Phthisiology in the West of the Country.

PurposeRomania is one of the European countries that has been hit the hardest by the severe acute respiratory syndrome caused by the new coronavirus SARS-CoV-2, with over 1.91 million reported cases and over 59,257 deaths. The aim of this study was to identify the main predictors of death in hospitalized patients.Patients and MethodsIn the period from 1 March 2020 to 30 June 2021, an observational, retrospective, randomized, case-control study was conducted, which included a sample of 139 patients who died in hospital and another sample of 275 patients who had been discharged in an improved or healed condition. Confirmation of COVID-19 cases was performed by RT-PCR from nasopharyngeal and oropharyngeal exudates. Statistical data were analyzed by logistic regression, Cox regression and a comparison of survival curves by the log-rank (Mantel-Cox) test.ResultsThe most powerful logistic regression model identified the following independent predictors of death: history of coagulopathy HR = 30.73 [1.94–487.09], p = 0.015; high percentage of neutrophils HR = 1.09 [1.01–1.19], p = 0.027; and decreased blood-oxygenation HR = 53881.97 [1762.24–1647489.44], p < 0.001. Cox regression identified the following factors that influenced the evolution of cases: history of coagulopathy HR = 2.44 [1.38–4.35], p = 0.000; O2 saturation HR = 0.98 [0.96–0.99], p = 0.043; serum creatinine HR = 1.23 [1.08–1.39], p = 0.001; dyspnea on admission HR = 2.99 [1.42–6.30], p = 0.004; hospitalization directly in the ICU HR = 3.803 [1.97–7.33], p < 0.001; heart damage HR = 16.76 [1.49–188.56], p = 0.022; and decreased blood-oxygenation HR = 35.12 [5.92–208.19], p < 0.001.ConclusionKnowledge of the predictors of death in hospitalized patients allows for the future optimization of triage and therapeutic case management for COVID-19.

Linking soil N dynamics and plant N uptake by means of sensor support

Monitoring the spatial and temporal plant availability of nitrogen (N) in agroecosystems is a key step to improve the synchronization between N fertilizer application and crop N demand, consequently reducing the risk of N emissions to the environment. Using a winter wheat N fertilization dataset from six site-years, we linked dynamic nitrate data measured in the soil solution to standard soil and crop analyses data and multispectral imagery acquired by an unmanned aerial vehicle. Wheat N uptake was determined as remotely estimated N uptake (REN) from the spectral data with a power regression model (mean absolute error = 17 kg N ha−1). The nitrate-N in the soil solution (NSS), extracted by means of suction cups, was measured with an ion-selective electrode. The REN proved to be suitable for monitoring the accumulation of N in the plants along the season. The NSS was characterized by low values and found of limited use as a direct indicator for potentially plant-available N. The N balances resulted in N surplus in the range of 43–100 kg N ha−1 over the six site-years. The most important contribution to the N balances was the soil N supply (67–143 kg N ha−1; mineralization and atmospheric input). Including this factor in the fertilization strategy was investigated post-season by calculating the ‘adjusted N fertilization norm’, reflecting the current best fertilization practice in Switzerland. The approach suggested lower N fertilization rates in the fields with higher N surplus. However, such static empirical strategies do not allow to react to in-season changes. Sensor-based monitoring could help to overcome this shortcoming.

LEAF AREA OF Erythrina velutina Willd. (FABACEAE) THROUGH ALLOMETRIC EQUATIONS

Leaf area estimation is of fundamental importance to evaluate growth, development, and propagation of plants. This work aimed to adjust and identify regression models to estimate leaf area of Erythrina velutina from linear dimensions of the central leaflet of leaves. Two hundred simple leaves were collected from E. velutina mother plants in forest fragments in the municipality of Mossoró, Rio Grande do Norte state, Northeast Brazil. The equation for estimating leaf area of E. velutina was adjusted from linear, linear without intercept (0.0), quadratic, cubic, power, and exponential regression models. The best equations were those with the highest determination coefficient (R²), Pearson's correlation coefficient (r), Willmott's index ( d ) and CS index (CS), and with the lowest Akaike information criterion (AIC), mean absolute error (MAE), root mean square error (RMSE), and BIAS index closest to zero (BIAS). The equations fitted using the product of length by width (LW) showed the best criteria for estimating leaf area, thus best fitting the regression models used. Therefore, the equation ŷ = 1.4755*LW adjusted using the linear model without intercept is the most suitable for estimating leaf area of E. velutina (R² = 0.9906; r = 0.9860; d = 0.9929; CS = 0.9790; AIC = 1402.7; MAE = 5.34; RMSE = 7.90; BIAS = 0.019).

Home range patterns of Helmeted Woodpecker (Celeus galeatus), Lineated Woodpecker (Dryocopus lineatus), and Robust Woodpecker (Campephilus robustus) in Misiones, Argentina, in a global perspective

Home range mapping studies of birds inform about area requirements and responses to land management as reflected by home range sizes and by resource selection within home ranges. Tracking studies of woodpeckers (<em>Picidae</em>) so far have been concentrated in temperate regions. In the subtropical Atlantic Forest of northeast Argentina, we assessed interspecific differences in home range sizes in old-growth forests and selectively logged forests of Helmeted Woodpecker (<em>Celeus galeatus</em>, a globally threatened species), Lineated Woodpecker (<em>Dryocopus lineatus</em>), and Robust Woodpecker (<em>Campephilus robustus</em>). Helmeted Woodpecker had larger breeding home ranges in selectively logged forests, averaging 105 ± 39 ha for pairs, versus 60 ± 13 ha in old-growth forests. Lineated Woodpecker breeding home ranges of pairs averaged 56 ± 22 ha, and those of Robust Woodpecker 43 ± 22 ha, with no differences between forest types. Helmeted Woodpeckers had an unusual separation between the home ranges of males and females in breeding pairs, with a mean area overlap of only 8% ± 9% near the nest tree, resulting in large home ranges for pairs. Helmeted Woodpecker and Robust Woodpecker individuals that were followed into the post-breeding stage had marked expansions of their home range sizes relative to breeding home ranges. To place our findings with Atlantic Forest woodpeckers in perspective we reviewed whether woodpecker home range sizes increase with latitude and body mass globally. For 29 populations of 22 woodpecker species, a power regression model with these factors explained 24.1% of variation in breeding home range sizes, with 17.1% of variation explained by latitude alone. Woodpecker species with larger home ranges than predicted values were three species of North American woodpeckers of coniferous forests, as well as the Helmeted Woodpecker. Our results of smaller home ranges in old-growth forests for the Helmeted Woodpecker affirm an association of this species with such forests. We urge the conservation of the few remaining tracts of old-growth Atlantic Forest and more restoration of logged forests to mature conditions.

Allometric equations to estimate the leaf area of Psychotria colorata (Willd. Ex Schult.) Müll.Arg.

Estimating leaf area using non-destructive methods from regression equations has become a more efficient, quick, and accurate way. Thus, this study aimed to propose an equation that significantly estimates the leaf area of Psychotria colorata (Rubiaceae) through linear leaf dimensions. For this purpose, 200 leaves of different shapes were collected, and length (L), width (W), product of length by width (L.W), and real leaf area (LA) of each leaf blade were determined. Then, equations were adjusted for predicting leaf area using simple linear, linear (0.0), quadratic, cubic, power, and exponential regression models. The proposed equation was selected according to the coefficient of determination (R²), Willmott's agreement index (d), Akaike's information criterion (AIC), mean absolute error (MAE), mean squared error (RMSE) and BIAS index. It was noted that the equations adjusted using L.W met the best criteria for estimating leaf area, but the equation LA = 0.59 * L.W from linear regression without intercept was the most suitable. This equation predicts that 59% of leaf area is explained by L.W. Concluding, the leaf area of P. colorata can be estimated using an allometric equation that uses linear leaf blade dimensions.

Estimation of body weight in captive Amur tigers (Panthera tigris altaica).

So far, there has been no safe and convenient method to weigh the large fierce animals, like Amur tigers. To address this problem, we built models to predict the body weight of Amur tigers based on the fact that body weight is proportional to body measurements or age. Using the method of body measurements, we extracted the body measurements from 4 different kinds of the lateral body image of tigers, that is, total lateral image, central lateral image, ellipse fitting image, and rectangle fitting image, and then we respectively used artificial neural network (ANN) and power regression model to analyze the predictive relationships between body weight and body measurements. Our results demonstrated that, among all ANN models, the model built with rectangle fitting image had the smallest mean square error. Comparatively, we screened power regression models which had the smallest Akakai information criteria (AIC). In addition, using the method of age, we fitted nonlinear regression models for the relationship between body weight and age and found that, for male tigers, logistic model had the smallest AIC. For female tigers, Gompertz model had the smallest AIC. Consequently, this study could be applied to estimate body weight of captive, or even wild, Amur tigers safely and conveniently, helping to monitor individual health and growth of the Amur tiger populations.