Machine Learning-Based Lithium Battery State of Health Prediction Research by Kun Li, Xinling Chen

“…The models were validated using the NASA PCoE battery aging datasets B0005, B0006, and B0007, with prediction accuracy evaluated based on Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R²). …”
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Rolling Prediction of Emergency Supplies Based on Postdisaster Multisource Time-Varying Information by Wei Li, Ming Zhang, Boquan Li, Songrui Li, Zhifeng Qiu

“…Finally, the proposed method is verified by an experiment with a general mean prediction error of 10.96%. However, the general mean prediction error of SVM reaches 17.77% in the static multistep prediction. …”
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Filter-Based Feature Selection Using Information Theory and Binary Cuckoo Optimisation Algorithm by Ali Muhammad Usman, Umi Kalsom Yusof, Maziani Sabudin

“…Dimensionality reduction is among the data mining process that is used to reduce the noise and complexity of features in various datasets. …”
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Temperature-Influenced SOC Estimation of LiFePO₄ Batteries in Hybrid Electric Tractors Based on SAO-LSTM Model by Yiwei Wu, Xiaohui Liu, Jingyun Zhang, Mengnan Liu, Lin Wang, Xiaoxiao Du, Xianghai Yan

“…The proposed SAO-LSTM model demonstrated superior SOC estimation performance compared to traditional ampere-hour integration, achieving a 98.23% error reduction. Evaluation results showed 0.39% and 0.31% decreases in root mean square error and mean absolute error, respectively, confirming the model’s robustness and high estimation accuracy for LiFePO₄ batteries in hybrid electric tractors.…”
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Robust Photovoltaic Power Forecasting Model Under Complex Meteorological Conditions by Yuxiang Guo, Qiang Han, Tan Li, Huichu Fu, Meng Liang, Siwei Zhang

“…However, traditional PV power forecasting models designed for distributed PV power stations often struggle with accuracy due to unpredictable meteorological variations, data noise, non-stationary signals, and human-induced data collection errors. To effectively mitigate these limitations, this work proposes a dual-stage feature extraction method based on Variational Mode Decomposition (VMD) and Principal Component Analysis (PCA), enhancing multi-scale modeling and noise reduction capabilities. …”
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Predicting the Tensile Strength of Plant Leaves Based on GA-SVM by Wei Chang, Meihong Liu, Yayu Huang, Junjie Lei, Kai Wu

“…A comparative analysis with other predictive algorithms demonstrates that the GA-SVM model achieves the lowest prediction error and highest accuracy, with mean absolute error and root mean squared error values of 0.0774 and 0.0745, respectively. …”
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Hybrid Improved PSO Algorithm for Soil Property Parameter Estimation by Mude Li, Aiping Shi, Yefan Shi, Tao Zhang, Cu Qu, Lihua Ye

“…For multimodal functions (e.g., F3), PSO-EDO significantly outperforms PSO-WOA (Particle Swarm Optimization-Whale Optimization Algorithm) with a 22.3% reduction in error. Simulation experiments further validate its engineering practicality: in soil parameter estimation, PSO-EDO completes 1000 iterations in just 1.95 s, with key parameters (e.g., sinkage coefficient n) controlled within a 7.32% error margin. …”
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Time-Frequency Analysis and Neural Network-Based Multipath Mitigation Method for Precise Positioning by Min-Ji Kim, O-Jong Kim, Jikang Lee, Changdon Kee, Juhyun Maeng

“…Experiments conducted under challenging indoor conditions demonstrate an approximately 30% reduction in the positioning error after applying the proposed multipath mitigation methods.…”
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Impact of Data Assimilation Frequency and Observation Location in Thermal Effluent Modeling for Coastal Waters by N. Alsulaiman, M. vanReeuwijk, M. D. Piggott

“…A series of observing system simulation experiments were conducted using the OpenDA toolbox to identify the optimal observation location and DA frequency. Significant reductions in temperature prediction errors were achieved using the EnKF for state estimation. …”
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INVARIANT SYSTEM OF AUTOMATIC WATER-LEVEL REGULATING IN THE BOILER SHELL by G. T. Kulakov, A. N. Kuchorenko

“…The comparative analysis of the modeling results of the Cascade-System Automatic Regulation (CSAR) with PID-regulator adjusted according to the foreign  methods  and  of  the  proposed  invariant  system  shows  considerable  improvement in regulation quality of the latter, viz. : system performance grows 2,5 times when working through the task jump, the peak value of overcorrection lowers from 42,5 to 10,0 %; while working through the internal disturbance, the regulating time reduces by 33 %, the maximum dynamic error of the regulation lowers by 65 %; the time of external combustion disturbance workout completion reduces two times, the maximum dynamic error of regulating – by 63 %; the maximum dynamic error of regulation while working through external disturbance with overheated steam rate diminishes by 71 %, the regulating time reduces by 1,5 times.…”
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Optimizing load demand forecasting in educational buildings using quantum-inspired particle swarm optimization (QPSO) with recurrent neural networks (RNNs):a seasonal approach by Sunawar Khan, Tehseen Mazhar, Tariq Shahzad, Tariq Ali, Muhammad Ayaz, Yazeed Yasin Ghadi, EL-Hadi M. Aggoune, Habib Hamam

“…Performance indicators, including Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE), were used to assess the models. …”
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The effect of transcranial random noise stimulation on the movement time and components of noise, co-variation, and tolerance in a perceptual-motor task by Fatemeh Salehi, Mohammadreza Doustan, Esmaeel Saemi

“…Moreover, the results indicate that tRNS elicited a significant reduction in both spatial error and movement execution time, (p ≤ 0.05). …”
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Enhancing Human–Computer Interaction With Cultural Nuance: A Deep Reinforcement Learning Perspective by Xiaohui Wang

“…The results of the studies show that adding cultural backgrounds to temperament recognition can result in significant gains, as demonstrated by a 4.6% reduction in detection error over previous models. …”
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Optimal Design of High-Precision Focusing Mechanism Based on Flexible Hinge by Zhanwei Huo, Guangzhen Li, Luyang Tan, Tianwen Yang, Dapeng Tian, Ji Li

“…This study indicates that the inclusion of a flexible hinge in the focusing mechanism leads to a substantial decrease in tilt error.…”
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Model-Free Predictive Current Controller for Common Mode Voltage Stabilization by Finite odd Virtual Vector set by Majid Akbari, S. Alireza Davari, Reza Ghandehari, Freddy Flores-Bahamonde, Jose Rodriguez

“…This error can significantly raise the total harmonic distortion (THD) output current of the inverter. …”
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Online Tool Wear Monitoring via Long Short-Term Memory (LSTM) Improved Particle Filtering and Gaussian Process Regression by Hui Xu, Hui Xie, Guangxian Li

“…By incorporating a nonlinear mean function based on machining parameters, the method effectively models the coupling relationships between cutting depth, spindle speed, feed rate, and wear, leading to a 31.09% reduction in MAE and a 42.61% reduction in RMSE compared to traditional linear models. …”
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Verificação das relações de Rateaux pelo emprego de um inversor de freqüência Verification of the relationships of Rateaux utilizing a frequency invertor by Aylton J. Alves, Luiz F. C. de Oliveira, Antônio M. de Oliveira

“…When the power consumption was evaluated using the relationships of Rateaux, the mean error in the reduction of power was 1.33 and 2.00% for 1500 and 1100 rotations min-1, respectively, which shows that the Rateaux relationships may be used for the estimation of manometric height, discharge and the power consumed from the characteristic curve obtained experimentally.…”
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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

“…Quantitative findings showed a significant reduction in psychological distress and an increase in compassion satisfaction during the post-earthquake supervision period (b = -0.18, error = 0.06, CrI = -0.29, -0.07, b = 0.26, error = 0.04, CrI = 0.18, 0.35, respectively). …”
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Intelligence data acquisition based on embedded system in Chinese cuisine cooker (CCICR V1.0) by Jianbao Zhang, Deyi Wang, Shiping Bao, Xin Chang, Yi Liang

“…The weighing module demonstrates a maximum relative error of only 0.288%, while the attitude sensor experiment shows an attitude information error of just 0.22°C. …”
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Convolutional neural network-based deep learning for landslide susceptibility mapping in the Bakhtegan watershed by Li Feng, Maosheng Zhang, Yimin Mao, Hao Liu, Chuanbo Yang, Ying Dong, Yaser A. Nanehkaran

“…The CNN model outperformed other classification approaches, achieving an accuracy of 95.76% and a precision of 95.11%. Additionally, error metrics confirmed its reliability, with a mean absolute error (MAE) of 0.11864, mean squared error (MSE) of 0.18796, and root mean squared error (RMSE) of 0.18632. …”
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