A low resistance circular diverter tee based on an improved random forest model by Ao Tian, Angui Li, Ran Gao, Ruoyin Jing, Yi Wang, Yan Tian, Yibu Gao, Junkai Ren, Yingying Wang

“…Unlike existing studies on local component resistance reduction that rely on trial-and-error empirical methods, this study introduces a posterior optimization approach that can obtain a global optimal solution within a given range. …”
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Personalized prediction model generated with machine learning for kidney function one year after living kidney donation by Rikako Oki, Toshihio Hirai, Kazuhiro Iwadoh, Yu Kijima, Hiroyuki Hashimoto, Yasunori Nishimura, Taro Banno, Kohei Unagami, Kazuya Omoto, Tomokazu Shimizu, Junichi Hoshino, Toshio Takagi, Hideki Ishida, Toshihito Hirai

“…Abstract Living kidney donors typically experience approximately a 30% reduction in kidney function after donation, although the degree of reduction varies among individuals. …”
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Design and Testing of a Tractor Automatic Navigation System Based on Dynamic Path Search and a Fuzzy Stanley Model by Bingbo Cui, Xinyu Cui, Xinhua Wei, Yongyun Zhu, Zhen Ma, Yan Zhao, Yufei Liu

“…Whole-field autonomous navigation showed that the maximum lateral tracking error was improved from 34 cm for the SM to 27 cm for the FSM, a reduction of approximately 20.6%, illustrating the superiority of the FSM in the application of whole-field path tracking. …”
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Stress and strength analysis of aluminum alloy structures under the effect of thermal and mechanical force by HE Zhiquan, QIU Huihui, SUN Yuheng, GUO Yujie, WEI Xiaohui

“…A set of modeling method and thermal stress measurement test technology under the combined action of heat and force were established. The maximum error between the simulation and test results of aluminum alloy sheet is 10%. …”
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Improving prediction accuracy in agricultural markets through the CIMA-AttGRU model. by Yankun Jiang, Jinhui Liu, Xiaotuan Li

“…Our empirical results demonstrate a significant improvement in forecasting precision, with the CIMA-AttGRU model achieving a Mean Absolute Error (MAE) reduction of 15% and a Mean Squared Error (MSE) reduction of 20% compared to conventional models. …”
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Enhancing Supply Chain Efficiency Resilience Using Predictive Analytics and Computational Intelligence Techniques by Lixing Bo, Jie Xu

“…The Transformer model achieved a reduction in Mean Absolute Error (MAE) from 15.8 to 8.2 and Root Mean Squared Error (RMSE) from 22.3 to 11.5, demonstrating enhanced forecasting accuracy. …”
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Enhancing Streamflow Prediction Accuracy: A Comprehensive Analysis of Hybrid Neural Network Models with Runge–Kutta with Aquila Optimizer by Rana Muhammad Adnan, Wang Mo, Ahmed A. Ewees, Salim Heddam, Ozgur Kisi, Mohammad Zounemat-Kermani

“…Results show that the LSTM-RUNAO model outperformed conventional ANN methods, achieving a 28.7% reduction in root mean square error (RMSE) and a 20.3% reduction in mean absolute error (MAE) compared to standard ANN models. …”
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Simulation of incremental update of electronic document information based on big data technology by Zhiyuan Jin, Qi Zhang, Tiejun Pan

“…Abstract Experimental results show that the algorithm converges faster and has a smaller average error compared to the BP algorithm and the attribute reduction algorithm. …”
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Designing a Neural Observer to Estimate the State Variables of the Dynamical System of a Specific Class of Leukaemia by Yousef Farshidi, Reza Ghasemi, Aminin Sharafian Ardekani

“…In order to adjust the neural network weights, the error back propagation learning algorithm was implemented. …”
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Study of Flight Departure Delay and Causal Factor Using Spatial Analysis by Shaowu Cheng, Yaping Zhang, Siqi Hao, Ruiwei Liu, Xiao Luo, Qian Luo

“…The study first explores the delay aggregation pattern by measuring and quantifying the spatial dependence of delay. The spatial error model (SEM) and spatial lag model (SLM) are then established to solve the error correlation and the variable lag effect, respectively. …”
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差动丝杠机构的传动性能 by 陈曼龙

“…As a new transmission parts,Differential Roller Screw（DRS）has great reduction ratio.Through introducing principle of this screw and analyzing its motion performance,the parameter affected DRS’s motion characteristic is discussed and the equation of DRS’s motion relation is deduced.And the DRS ’s efficiency and transmission error are investigated.…”
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Residential Electrical Load Forecasting Based on a Real-Time Evidential Time Series Prediction Method by M. Mroueh, M. Doumiati, C. Francis, M. Machmoum

“…Application of this method to publicly available load datasets demonstrates its effectiveness, achieving a 12% reduction in forecasting error compared to state-of-the-art methods and delivering substantial improvements in computational efficiency.…”
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Neural Learning Control of Flexible Joint Manipulator with Predefined Tracking Performance and Application to Baxter Robot by Min Wang, Huiping Ye, Zhiguang Chen

“…To facilitate the design, a new transformed function is introduced to convert the constrained tracking error into unconstrained error variable. Then, a novel adaptive neural dynamic surface control scheme is proposed by combining the neural universal approximation. …”
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Efficient multi-station air quality prediction in Delhi with wavelet and optimization-based models. by Lakshmi Sankar, Krishnamoorthy Arasu

“…The AquaWave-BiLSTM framework demonstrated exceptional predictive accuracy, with a Mean Squared Error (MSE) of 0.00065, a Mean Absolute Error (MAE) of 0.04566, a Root Mean Square Error (RMSE) of 0.02523, and an R² value of 0.9494, surpassing conventional methodologies. …”
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Development and Evaluation of a LiFi-Transceiver Module for TMTC Intra-Satellite Communication by Marek Jahnke, Benjamin Palmer, Ulf Kulau

“…The use of Light Fidelity (LiFi) can enable the reduction of satellite mass by reducing the wiring harness while avoiding electromagnetic interference. …”
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Predictive Model for Erosion Rate of Concrete Under Wind Gravel Flow Based on K-Fold Cross-Validation Combined with Support Vector Machine by Yanhua Zhao, Kai Zhang, Aojun Guo, Fukang Hao, Jie Ma

“…It showed average relative errors of 22% and 31.6% for the Bitter and Oka models, respectively. …”
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Optimization of Sensor Targeting Configuration for Intelligent Tire Force Estimation Based on Global Sensitivity Analysis and RBF Neural Networks by Yu Zhang, Guolin Wang, Haichao Zhou, Jintao Zhang, Xiangliang Li, Xin Wang

“…Three intelligent tire force estimation models with different sensor-targeting configurations were established using radial basis function (RBF) neural networks. The mean relative error (MRE) of intelligent tire force estimation for these models remained within 10%, with Model 3 demonstrating an MRE of less than 2% and estimation errors of 1.42% and 1.10% for longitudinal and lateral forces, respectively, indicating strong generalization performance. …”
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A Hybrid ARIMA-LSTM-XGBoost Model with Linear Regression Stacking for Transformer Oil Temperature Prediction by Xuemin Huang, Xiaoliang Zhuang, Fangyuan Tian, Zheng Niu, Yujie Chen, Qian Zhou, Chao Yuan

“…Experimental results demonstrate the hybrid model’s superiority: In 5000-data-point prediction, it achieves an MSE = 0.9908 and MAPE = 1.9824%, outperforming standalone XGBoost (MSE = 3.2001) by 69.03% in error reduction and ARIMA-LSTM (MSE = 1.1268) by 12.08%, while surpassing naïve methods 1–2 (MSE = 1.7370–1.6716) by 42.94–40.74%. …”
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Improving Precision and Reducing Runtime of Microscopic Traffic Simulators through Stratified Sampling by Khewal Bhupendra Kesur

“…LHS is found to outperform AV, and reductions of up to 71% in the standard error of estimates of traffic network performance relative to independent sampling are obtained. …”
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Image Denoise based on Undecimated Wavelet Transform: A Comparative Analysis by Israa Hashim Latif

“…The initial section examined the performance of Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) utilizing MATLAB, indicating that biorthogonal wavelets provide optimal noise reduction with minimal degradation of detail. …”
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