Compositional modeling of solution gas–oil ratio (Rs): a comparative study of tree-based models, neural networks, and equations of state by Aydin Larestani, Sara Sahebalzamani, Abdolhossein Hemmati-Sarapardeh, Ali Naseri

“…A comprehensive database of 1,154 data points was utilized for modeling. Among the tested models, the extra trees (ET) algorithm demonstrated superior performance, achieving an average absolute percent relative error (AAPRE) of approximately 3%, indicating its high reliability for Rs prediction. …”
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A Statistical Optimization Method for Sound Speed Profiles Inversion in the South China Sea Based on Acoustic Stability Pre-Clustering by Zixuan Zhang, Ke Qu, Zhanglong Li

“…The SSP inversion experimental results show that: (1) the SSP samples of the South China Sea can be divided into three clusters of disturbance modes with statistically significant differences. (2) The regression inversion method based on cluster attribution reduces the average error of SSP inversion for data from 2018 to 1.24 m/s, which is more than 50% lower than what can be achieved with the traditional method without pre-clustering. (3) Transmission loss prediction verification shows that the proposed method can produce highly accurate sound field calculations in environmental assessment tasks. …”
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Population substructure and control selection in genome-wide association studies. by Kai Yu, Zhaoming Wang, Qizhai Li, Sholom Wacholder, David J Hunter, Robert N Hoover, Stephen Chanock, Gilles Thomas

“…A panel of 12,898 autosomal SNPs common to both the Illumina and Affymetrix commercial platforms and with low local background linkage disequilibrium (pair-wise r(2)<0.004) was selected to infer population substructure with principal component analysis. A novel permutation procedure was developed for the correction of PS that identified a smaller set of principal components and achieved a better control of type I error (to lambda of 1.032 and 1.006, respectively) than currently used methods. …”
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Angle Expansion Estimation and Correction Based on the Lindeberg–Feller Central Limit Theorem Under Multi-Pulse Integration by Jiong Cai, Rui Wang, Handong Yang

“…Subsequently, this paper conducts a detailed analysis of the impact of the amplitude fluctuations and target maneuvers on the random angle measurement error. …”
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Prediction model for China's monthly LNG ex-factory prices based on BP-ARIMA by Xueping DU, Qinghua ZHAO, Lin MI, Zhikai LANG, Menglin LIU, Jiangtao WU

“…Accurately predicting these price trends is vital for optimizing the LNG industry chain layout and enhancing the economic efficiency of the natural gas supply chain. Methods Historical data on China's LNG ex-factory prices were collected and analyzed, identifying key factors that influence price changes through grey relational analysis. …”
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Fish food security: analyzing the economic and environmental variables by Alaa Ahmed Kotb, Emad S. Aljohani, Yosef Alamri, Abdullah I. Aldakhil, Fuad Alagsam, Mansour Abdulaziz Alobaid, Mahdi Alsultan

“…The study employs descriptive statistical analysis to identify trends, patterns, and outliers in the data, using measures such as means, standard deviations, and growth rates. …”
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Multifractal-Aware Convolutional Attention Synergistic Network for Carbon Market Price Forecasting by Liran Wei, Mingzhu Tang, Na Li, Jingwen Deng, Xinpeng Zhou, Haijun Hu

“…The fractal attention (FA) module calculates similarity matrices within a multi-scale feature space through multi-head attention, adaptively integrating multifractal market dynamics and implicit associations. The dynamic error correction (DEC) module models error commonality through variational autoencoder (VAE), and uncertainty-guided dynamic weighting achieves robust error correction. …”
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Advanced neural network modeling with Levenberg–Marquardt algorithm for optimizing tri-hybrid nanofluid dynamics in solar HVAC systems by A. Aziz, S.A.H. Shah, H.M.S. Bahaidarah, T. Zamir, T. Aziz

“…The model’s performance is evaluated using state transition (ST) index, error histogram (EH), mean squared error, and regression (R) analysis, demonstrating excellent agreement between predicted and reference solutions. …”
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Predictive modeling of coagulant dosing in drilling wastewater treatment using artificial neural networks by Mahyar Kalhormohammadi, Sanaz Khoramipour

“…In terms of error, the ELMs model demonstrated the lowest RMSE values for both coagulant (0.13) and flocculant (0.10) predictions. …”
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Application of Gradient Boosting Machine Learning Algorithms to Predict Uniaxial Compressive Strength of Soft Sedimentary Rocks at Thar Coalfield by Niaz Muhammad Shahani, Muhammad Kamran, Xigui Zheng, Cancan Liu, Xiaowei Guo

“…According to the results, the XGBoost algorithm outperformed the GBR, Catboost, and LightGBM with coefficient of correlation (R2) = 0.99, mean absolute error (MAE) = 0.00062, mean square error (MSE) = 0.0000006, and root mean square error (RMSE) = 0.00079 in the training phase and R2 = 0.99, MAE = 0.00054, MSE = 0.0000005, and RMSE = 0.00069 in the testing phase. …”
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Estimation of Weibull distribution parameters to assess the wind energy potential of high altitude sites in the Andean region of Ecuador by Natalia Alexandra Pérez Londo, Darwin Saúl Lema Londo, Rosa Ormaza Hugo, Fabián Londo, Narcisa Sánchez Salcán, Diana Katherine Campoverde-Santos, Dalinda Quingatuña, Julio Coello-Cabezas

“…To evaluate the accuracy of these methods, the mean square error, the root mean square error, the mean absolute error, the coefficient of determination, and the chi-square test were used. …”
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An electrical load forecasting model based on a novel closed loop neural networks and interaction gain feature selection by Gholamreza Memarzadeh, Faezeh Amirteimoury, Hossein Noori, Farshid Keynia

“…This multi-step process begins with the wavelet transform, which decomposes the load data into distinct frequency components, allowing for a detailed analysis of underlying patterns. …”
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A hybrid mutational Northern Goshawk and elite opposition learning artificial rabbits optimizer for PEMFC parameter estimation by Pradeep Jangir, Absalom E. Ezugwu, Kashif Saleem, Arpita, Sunilkumar P. Agrawal, Sundaram B. Pandya, Anil Parmar, G. Gulothungan, Laith Abualigah

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Augmented robustness in home demand prediction: Integrating statistical loss function with enhanced cross-validation in machine learning hyperparameter optimisation by Banafshe Parizad, Ali Jamali, Hamid Khayyam

“…This method integrates statistical analysis of k-fold validation errors by incorporating their mean and variance into the optimization objective, enhancing robustness and generalizability. …”
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Prediction of mechanical characteristics of shearer intelligent cables under bending conditions. by Lijuan Zhao, Dongyang Wang, Guocong Lin, Shuo Tian, Hongqiang Zhang, Yadong Wang

“…The results show that, compared to other predictive models, the proposed model achieves reductions in Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) to 0.0002, 0.0159, and 0.0126, respectively, with the coefficient of determination (R2) increasing to 0.981. …”
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Design and experiment of light angle adjustable device for measuring sugar content of citrus by near infrared spectroscopy by ZENG Xianming, HAN Longbo, WEN Tao, DAI Xingyong

“…When the illumination angle was 30°, the citrus sugar prediction model based on the original transmittance spectrum data had the best effect. At this time, the correlation coefficient of the prediction set, the root mean square error of the prediction set, the correlation coefficient of the correction set, and the root mean square error of the correction set were 0.887 6, 0.897 5, 0.456 0, 0.430 9 °Brix, respectively. …”
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Predictive modeling of telemedicine implementation in central Asia using neural networks by Zhannur ABDRAKHMANOVA, Talgat DEMESSINOV, Kadisha JAPAROVA, Monika KULISZ, Gulzhan BAYTIKENOVA, Ainur KARIPOVA, Zhansaya ERSAINOVA

“…To identify the factors that contribute to telemedicine adoption, a dataset of epidemiological, demographic, and digital infrastructure indicators was analyzed. For the analysis, data from the National Statistical Office of the Republic of Kazakhstan (2014-2024) were used. …”
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Study of Aerosol Influence on Nighttime Land Surface Temperature Retrieval Based on Two Methods by Caixia Gao, Enyu Zhao, Chuanrong Li, Yonggang Qian, Lingling Ma, Lingli Tang, Xiaoguang Jiang, Hongyuan Huo

“…The aim of this study is to evaluate the aerosol influence on LST retrieval with two algorithms (split-window (SW) method and a four-channel based method) using simulated data under typical conditions. The results show that the root mean square error (RMSE) decreases to approximately 2.3 K for SW method and 1.5 K for four channel based method when VZA = 60° and visibility = 3 km; an RMSE would be increased by approximately 1.0 K when visibility varies from 3 km to 23 km. …”
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Influence of artificial intelligence on higher education reform and talent cultivation in the digital intelligence era by Limin Qian, Weiran Cao, Lifeng Chen

“…The results show that SEOM has high accuracy and generalization ability in three different teaching scenes: online mixed teaching, personalized teaching and project-based teaching. The Root Mean Square Error (RMSE) value in cross-validation is between 0.2 and 0.5, and the Mean Absolute Error (MAE) value is between 0.1 and 0.5. …”
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FAILURE PREDICTION OF BOLTED CONNECTION OF COMPOSITE MATERIALS BASED ON DEEP LEARNING (MT) by PENG Fan, ZOU SiNong, REN YiRu

“…Using finite element software, the data set of peak failure load of bolted laminates was calculated to construct the deep learning neural network. …”
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