What diameter? What height? Influence of measures of average tree size on area-based allometric volume relationships by Yilin Wang, John A. Kershaw, Mark J. Ducey, Yuan Sun, James B. McCarter

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A Novel Classification Method for Flutter Signals Based on the CNN and STFT by Shiqiang Duan, Hua Zheng, Junhao Liu

“…Necessary model calculation simplifications, uncertainty in actual wind tunnel test, and data acquisition system error altogether lead to error between a set of actual experimental results and a set of theoretical design results; wind tunnel test flutter data can be utilized to feedback this error. …”
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Applicability of formulas for calculating differential renal depth by Luo Jin, Deng Wei, Cao Jiang, Tian Jia-li, Wang Ying, Wang Rong, Li Yan-mei, Zhao Qian, Yang Ji-qin, Li Juan

“…Objective To evaluate the applicability of differential renal depth calculation formulas for Chinese people and provide references for selecting renal depth calculation formulas.Methods The SPECT/CT data were analyzed retrospectively for 234 patients with glomerular filtration rate measured by renal dynamic imaging from May to December 2018.CT depth was measured as the standard, correlation, average difference and 1 cm error rate between six renal depth calculation formulas and CT measurements were compared.Results Strong correlations existed between estimated values of six formulas and measured values of CT.Data analysis showed that the correlation coefficient between Lee’s equation and CT measured values was better than that of the other five formulas, r=0.737 for left kidney and 0.750 for right kidney.The renal depth obtained by Lee’s equation was closest to that measured by CT and the difference was not statistically significant（left kidney mean deviation 0.03 cm, right kidney mean deviation 0.08 cm）.The 1 cm error rate of Tonnesen formula was the largest.And it was 54.70% for left kidney and 57.69% for right kidney.The 1 cm error rates of the other five formulas were tested by X2 test and there was no statistical difference（P>0.05）.Conclusions No significant difference exists between left and right kidney depth calculated by Lee’s equation and the measured value of CT.Its deviation range is small and it is better than the other five formulas.A wider clinical popularization is worthwhile.…”
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SOH and RUL Estimation for Lithium-Ion Batteries Based on Partial Charging Curve Features by Kejun Qian, Yafei Li, Qiheng Zou, Kecai Cao, Zhongpeng Li

“…Experimental validation on public datasets demonstrates superior performance of the methodology described above, with an SOH estimation root mean square error (RMSE) and mean absolute error (MAE) below 1.42% and 0.52% and RUL estimation relative error (RE) under 1.87%. …”
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An enhanced iTransformer-based early warning system for predicting automotive rental contract breaches. by Ming Jiang, Dongpeng Peng, Haihan Yu, Shu Chen

“…The system identifies and prevents default risks in a timely manner through a comprehensive analysis of vehicle driving data, thereby safeguarding the interests of corporate entities. …”
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Deep learning models for hepatitis E incidence prediction leveraging Baidu index by Yanhui Guo, Li Zhang, Shengnan Pang, Xiya Cui, Xuechen Zhao, Yi Feng

“…The performance of models are evaluated by three standard quality metrics, including root mean square error(RMSE), mean absolute percentage error(MAPE) and mean absolute error(MAE). …”
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How to use learning curves to evaluate the sample size for malaria prediction models developed using machine learning algorithms by Sophie G. Zaloumis, Megha Rajasekhar, Julie A. Simpson

“…Prediction error was measured using the balanced error rate (average of percentage of slow clearing infections incorrectly predicted as fast and percentage of fast clearing infections predicted as slow). …”
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TRENDS AND DETERMINANTS OF PRICE IN THE POULTRY SUB-SECTOR OF NIGERIA by Joseph Evo EWA, Chigozirim Ndubuisi ONWUSIRIBE, Felix Chibueze NZEAKOR

“…Trend analysis, ADF unit root test, cointegration test error correction model, and impulse response were used to analyze the data. …”
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Cross-language orthographic neighborhood density effects in Dutch–English and Spanish–English bilinguals by Britta Biedermann, Britta Biedermann, Elisabeth Beyersmann, Mara Blosfelds, Christella Macapagal, Ashleigh Rosevear, Welber Marinovic

“…For Experiment 1, an analysis of generalized linear mixed-effects models (GLMMs) revealed that Dutch (L1)–English (L2) bilinguals showed a facilitatory main effect of English ND on reaction times and error rates. …”
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Enhancing Marshall stability of asphalt concrete using a hybrid deep neural network and ensemble learning by Henok Desalegn Shikur, Ming-Der Yang, Yared Bitew Kebede

“…Model performance was rigorously assessed using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Coefficient of Determination (R²), Mean Absolute Percentage Error (MAPE), and Coefficient of Variation of the Root Mean Square Error (CVRMSE) on both training and unseen testing datasets. …”
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Prediction of tablet disintegration time based on formulations properties via artificial intelligence by comparing machine learning models and validation by Mohammed Ghazwani, Umme Hani

“…Among the models, SBL stood out for its superior performance, achieving the highest R² scores and the lowest Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) error rates in both the training and testing phases. …”
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Problems in verb conjugation in Spanish among Malaysian Chinese students: A case study by Edison Mejia Vasquez

“…A distinction is made between "error" and "mistake" and how both terms are interrelated and apply to the data studied. …”
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Evaluation of Spatial Interpolation Methods for Wind Speed and Direction: A Case Study in Split-Dalmatia County by M. Radić, L. Šerić, M. Bugarić

“…The analysis revealed that the highest errors occurred during Bora wind conditions. …”
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Improved Electrochemical–Mechanical Parameter Estimation Technique for Lithium-Ion Battery Models by Salvatore Scalzo, Davide Clerici, Francesca Pistorio, Aurelio Somà

“…An error analysis—based on the Root Mean Square Error (RMSE) and confidence ellipses—confirms that the inclusion of mechanical measurements significantly improves the accuracy of the identified parameters and the reliability of the algorithm compared to approaches relying just on electrochemical data. …”
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Short-Term Daily Univariate Streamflow Forecasting Using Deep Learning Models by Eyob Betru Wegayehu, Fiseha Behulu Muluneh

“…Hence, in this study, we compared Stacked Long Short-Term Memory (S-LSTM), Bidirectional Long Short-Term Memory (Bi-LSTM), and Gated Recurrent Unit (GRU) with the classical Multilayer Perceptron (MLP) network for one-step daily streamflow forecasting. The analysis used daily time series data collected from Borkena (in Awash river basin) and Gummera (in Abay river basin) streamflow stations. …”
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Integration of MRMR algorithm with advanced neural networks for modeling long-term crop water demand in agricultural basins by Ahmed Elbeltagi, Abdullah A. Alsumaiei, Ali Raza, Mustafa Al-Mukhtar, Salim Heddam

“…Therefore, this study aims to achieve more accurate AET predictions through i) evaluating the performance of five artificial neural network (ANN) models optimized with the minimum redundancy maximum relevance (MRMR) algorithm to estimate monthly AET across diverse agroclimatic zones in China and ii) selecting the model with the highest accuracy based on performance metrics and minimal error between estimated and actual AET values. The analysis utilized weather data from Jinzhou, Anshan, Harbin, Shenyang, and Changchun from 1958 to 2021, with 75% of the data allocated for training and 25% for testing. …”
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Instrumentation System for Monitoring of Soil Variables in Precision Agriculture Applications by Diana Rueda-Delgado, Fredy Cuellar-Torres, Damian Martinez, Margarita Sofia Narducci

“…Results showed that for the Kriging technique, MAE (mean absolute error) and RMSE (root-mean-square error) values were lower, confirming that this method is adequate for extrapolation and soil data visualization.…”
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Evaluating Predictive Accuracy of Regression Models with First-Order Autoregressive Disturbances: A Comparative Approach Using Artificial Neural Networks and Classical Estimators by Rauf I. Rauf, Masad A. Alrasheedi, Rasheedah Sadiq, Abdulrahman M. A. Aldawsari

“…The study evaluates each method’s performance on three datasets characterized by autocorrelation, comparing their predictive accuracy and variability. The analysis is structured into three phases: the first phase examines predictive accuracy across methods using Mean Squared Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE); the second phase evaluates the efficiency of parameter estimation based on standard errors across methods; and the final phase visually assesses the closeness of predicted values to actual values through scatter plots. …”
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Flood Detection and Susceptibility Mapping Using Sentinel-1 Time Series, Alternating Decision Trees, and Bag-ADTree Models by Ayub Mohammadi, Khalil Valizadeh Kamran, Sadra Karimzadeh, Himan Shahabi, Nadhir Al-Ansari

“…We used Sentinel-1 data for flood detection and time series analysis. …”
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A secure and efficient user selection scheme in vehicular crowdsensing by Min Zhang, Qing Ye, Zhimin Yuan, Kaihuan Deng

“…The SEUS-VCS scheme has advantages in reducing loss function (Loss), Mean Square Error (MSE), and Mean Absolute Error (MAE), and the predicted results match the true data very well. …”
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