Prediction of properties of electroless nickel plating with diamond powder based on artificial neural network by Lili FANG, Han LIU, Yufei JIANG

“…The absolute relative error between the predicted coating performance values and experimental values of the GRNN model was less than 10.00%, with an average absolute relative error of 5.07%. …”
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A traceability model for upper corner gas in fully mechanized mining faces based on XGBoost-SHAP by SHENG Wu, WANG Lingzi

“…Finally, the model performance was evaluated using multi-source sensor monitoring data from the field. Case analysis results showed that: ① the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) of the XGBoost model were 0.93, 0.007, and 0.008, respectively, indicating the highest goodness of fit and the lowest errors compared with random forest (RF), support vector regression (SVR), and gradient boosting decision tree (GBDT). ② The mean relative error of the XGBoost model was 4.478%, demonstrating higher accuracy and better generalization performance compared with the other models. ③ Based on the mean absolute SHAP values of input features, the gas concentration at T1 on the working face had the greatest influence on the gas concentration in the upper corner, followed by the gas concentration in the upper corner extraction pipeline, with the gas content and roof pressure of the mining coal seam following closely. …”
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Prediction of the Residual Compressive Strength of Rice Husk Ash Concrete after Exposure to Elevated Temperatures Using XGBoost Machine Learning Algorithm by Elvis Ang'ang'o, Silvester Abuodha, Siphila Mumenya

“…The model accuracy was checked using statistical scores: coefficient of determination (R2), root mean squared error (RMSE), and mean absolute error (MAE). SHAP values were used for feature importance analysis. …”
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Testing the role of online group-based supervision for local humanitarian workers following a crisis: A mixed-methods longitudinal study. by Gülşah Kurt, Fatema Almeamari, Hafsa El-Dardery, Aya Kardouh, Scarlett Wong, Michael McGrath, Louis Klein, Ammar Beetar, Salah Lekkeh, Ahmed El-Vecih, Wael Yasaki, Ceren Acarturk, Dusan Hadzi-Pavlovic, Zachary Steel, Simon Rosenbaum, Ruth Wells

“…A piecewise mixed-effects model within a Bayesian Hierarchical framework was used to assess changes in outcomes across three periods: the active control period (7 months), pre-earthquake supervision period (3 months), and post-earthquake supervision period (6 months). The thematic analysis was used to analyze the qualitative data from the interviews and supervision sessions. …”
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Experimental studies on constant mass–volume depletion of gas-condensate systems by M. El Aily, M.H.M. Khalil, S.M. Desouky, M.H. Batanoni, M.R.M. Mahmoud

“…Statistical error analysis was used to determine the accuracy of the model. …”
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Development of a High-Speed Time-Synchronized Crop Phenotyping System Based on Precision Time Protoco by Runze Song, Haoyu Liu, Yueyang Hu, Man Zhang, Wenyi Sheng

“…This system can provide hardware support for multi-parameter acquisition and data registration in the fast mobile crop phenotype platform, laying a reliable data foundation for crop growth monitoring, intelligent yield analysis, and prediction.…”
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Suggesting a Novel Hybrid Approach for Predicting Solar Irradiance in the Qinghai Province of China by Baran Yılmaz, Rachel Samra

“…Many factors, such as the coefficient of determination, root mean square error, mean absolute percentage error, and mean absolute error have been used in presenting this work, and SMA-LSTM results with the lowest amount of R2 has illustrated acceptable performance.…”
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Skip-based combined prediction method for distributed photovoltaic power generation by WU Minglang, PANG Zhenjiang, HONG Haimin, ZHAN Zhaowu, JIN Fei, TANG Yuanyang, YE Xuan

“…In the feature extraction, we use statistical analysis, features cross-correlation, periodicity information, approximate entropy, and the temperature of PV panels to achieve deep feature extraction of time, weather, and power generation data, enriching the model inputs. …”
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A LightGBM-Based Power Grid Frequency Prediction Method with Dynamic Significance–Correlation Feature Weighting by Jie Zhou, Xiangqian Tong, Shixian Bai, Jing Zhou

“…This is achieved by constructing a joint screening mechanism of feature time series correlation analysis and statistical significance test, combined with the LightGBM gradient-boosting decision tree (GBDT) framework; accordingly, high-precision prediction of grid frequency time series data is realized. …”
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A method for English paragraph grammar correction based on differential fusion of syntactic features. by Weiling Liu, Caijun Zhao, Yongyi Li, Chenglong Cai, Hong Liu, Ruilin Qiu, Ruoci Su, Bingbing Li

“…Then carry out difference fusion analysis, measure the syntactic differences of adjacent sentences by cosine similarity, identify the significant differences caused by grammatical errors according to the preset threshold, lock the position and type of errors, and input the original sentence vector into the Seq2Seq model based on Transformer. …”
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基于R410A的板式换热器两相仿真计算模型 by 邱峰, 谷波, 曾伟平, 张春路

“…The two-phase simulation models for a plate heat exchanger with R410A are established,and the error analysis and the comparison for the models are performed using the experimental data.Moreover,the factors to influence the performance of the heat exchanger are discussed.The average error of the modified model of condensation heat transfer is less than 5%.The accuracy of the modified model of evaporation heat transfer based on Yan and Lin increases by 10% compared with those in literature and its average err...…”
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A Markerless Approach for Full-Body Biomechanics of Horses by Sarah K. Shaffer, Omar Medjaouri, Brian Swenson, Travis Eliason, Daniel P. Nicolella

“…All networks predicted over 78% of the markers within 25% of the length of the radius bone on test data. Root-mean-square-error (RMSE) between joint angles predicted via IK using ground truth marker-based motion capture data and network-predicted data was less than 10 degrees for 25 to 32 of 35 degrees of freedom, depending on the gait and data used for network training. …”
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Research on COP Prediction Model of Chiller Based on PSO-SVR by Zhou Xuan, Cai Panpan, Lian Sizhen, Yan Junwei

“…The relative error is within 5%. So this model can provide theoretical basis for the chiller energy efficiency analysis, fault detection and diagnosis and optimizing control.…”
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Evaluating LSTM and NARX neural networks for wind speed forecasting and energy optimization in Tetouan, Northern Morocco by Wissal Masmoudi, Abdelouahed Djebli, Faouzi Moussaoui

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Linear mixed effects models under inequality constraints with applications. by Laura Farnan, Anastasia Ivanova, Shyamal D Peddada

“…Constraints arise naturally in many scientific experiments/studies such as in, epidemiology, biology, toxicology, etc. and often researchers ignore such information when analyzing their data and use standard methods such as the analysis of variance (ANOVA). …”
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Study on Finite Element Model Modification of Long-Span Suspension Bridge Based on BPANN-GA by Zi-Xiu Qin, Xi-Rui Wang, Wen-Jie Liu, Zi-Jian Fan

“…In order to improve the reliability of the finite element analysis model of long-span suspension bridges, this paper proposes a finite element model (FEM) modification method by the hybrid algorithm of backpropagation artificial neural network (BPANN) and genetic algorithm (GA) based on field measurements and vibration modal analysis. …”
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Advanced Numerical Validation of Integrated Electrochemical-Thermal Models for PCM-Based Li-Ion Battery Thermal Management System by Mahdieh Nasiri, Hamid Hadim

“…Incorporating Capric acid (with a phase transition range of 302–305 K) as the PCM, the thermal management model demonstrates significantly improved accuracy over existing models in the literature. Notable error reductions include a 78.3% decrease in the Mean Squared Error (0.477 vs. 2.202), a 53.4% reduction in the Root Mean Squared Error (0.619 vs. 1.483), and a 55.5% drop in the Mean Absolute Percentage Error. …”
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Research on IGBT Sequentially Prediction Algorithm Based on Improved Wavelet Neural Network by Kexun HUANG, Songrong WU, Binan XIANG, Rui XU, Zhenwei TU

“…Aiming at the aging failure of IGBT, an improved wavelet neural network sequentially prediction method based on genetic algorithm was proposed. Based on the analysis of IGBT failure mechanism, with the IGBT aging data, the instantaneous collector emitter peak voltage was selected as the failure characteristic parameter, the training set and test set were constructed by the sliding time window method, and then the wavelet neural network prediction model improved by genetic algorithm was built in MATLAB for prediction, which was compared with the traditional wavelet neural network prediction model. …”
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Quantitative study of local defects by pulsed eddy current testing by Zhaoyang Li, Liang Dong

“…Analytical formulas are derived to describe the detection process, providing a foundation for subsequent error analysis. To optimize the detection process, four distinct types of error functions are formulated, and an optimization algorithm is employed to determine the parameters that minimize these error functions. …”
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Spread Spectrum OFDM-IM With Efficient Precoded Matrix and Low Complexity Detector by Ahmed Waheed, Somayeh Mohammady

“…The theoretical analysis based on pair-wise error probability is derived to estimate the bit error probability (BEP) of the proposed scheme for the performance analysis. …”
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