Control Strategy of In-Port U-Turn for Ships Based on Arctangent Function Nonlinear Feedback by Shihang Gao, Xianku Zhang

“…The incorporation of nonlinear feedback technology significantly reduces energy consumption and steering gear wear, with specific improvements including a reduction in the average rudder angle by up to 18.26%, a reduction in the mean absolute error (MAE) by up to 3.6%, a reduction in the mean integrated absolute (MIA) by up to 13.55%, and a reduction in the mean total variation (MTV) by up to 36.36%. …”
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Estimation of Manning\'s Roughness Coefficient by the Inverse Solving Method using Observational Data (Sanij River-Yazd, Iran) by Mahtab Alimoradi, Mohammad Reza Ekhtesasi, Arash malekian

“…Despite many efforts, the inability to accurately estimate the roughness coefficient and the use of Manning's constant value (n) are the main error factors in flood simulation and flow depth calculation. …”
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Deep Learning Model for Real‐Time Flood Forecasting in Fast‐Flowing Watershed by Fan Wang, Jie Mu, Cheng Zhang, Weiqi Wang, Wuxia Bi, Wenqing Lin, Dawei Zhang

“…This leads to an average increase in Nash efficiency of approximately 7.86% and a reduction in the interquartile range of relative peak error by about 30.7%. …”
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A SAR wave-enhanced method combining denoising and texture enhancement for bathymetric inversion by Aijun Cui, Yi Ma, Jingyu Zhang, Ruifu Wang

“…The proposed method improved bathymetric accuracy, reducing mean absolute error (MAE) by up to 4.69 m and mean relative error (MRE) by up to 18 %. …”
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Texture image segmentation based on geometric classification and assessment density of contour elements by H. M. Alzakki, V. Yu. Tsviatkou

“…The proposed method in comparison with the method based on energy maps, providing a reduction in the error of localization of textural regions by taking into account the geometric characteristics of the elements.…”
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Auto-expansion software prompting reduces abbreviation use in electronic hospital discharge letters: an observational pre- and post-intervention study by Shamus Toomath, Emily J Hibbert

“…This approach could significantly improve clarity of communication between hospital doctors and community healthcare professionals during patient care transition, potentially reducing medical errors.…”
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High-precision position control of hydraulic support pushing system based on quasi-sliding mode by GAO Yuhao, SUN Xing, LI Yang, LIU Wei, LI Jingyan

“…In the sinusoidal response, stable tracking was achieved within 0.2 s, with a peak error of about 0.001 m, representing a reduction of approximately 94.7%, and it exhibited broader bandwidth characteristics. …”
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End-to-end neural automatic speech recognition system for low resource languages by Sami Dhahbi, Nasir Saleem, Sami Bourouis, Mouhebeddine Berrima, Elena Verdú

“…Using synthetic speech and data augmentation techniques can enhance E2E-ASR performance for low-resource languages, reducing word error rates (WERs) and character error rates (CERs). …”
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Advancing smart communities with a deep learning framework for sustainable resource management. by Yongyan Zhao

“…Predictive models received assessment based on Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R². …”
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A Physics‐Informed Deep Learning Framework for Estimating Thermal Stratification in a Large Deep Reservoir by Yuan He, Xiaofan Yang

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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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Experimental and numerical study on axial compressive strength of waste carbon fibre-reinforced concrete by Umar Ayaz Lone, Bin Zhao, Danish Yousuf Wani, Chengxin Peng

“…Simulated results demonstrated excellent alignment with experimental data, achieving a maximum error of just 0.84%, validating the robustness of the model. …”
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An Underground Goaf Locating Framework Based on D-InSAR with Three Different Prior Geological Information Conditions by Kewei Zhang, Yunjia Wang, Feng Zhao, Zhanguo Ma, Guangqian Zou, Teng Wang, Nianbin Zhang, Wenqi Huo, Xinpeng Diao, Dawei Zhou, Zhongwei Shen

“…The quantitative performance results indicate that, (1) under a detailed prior information condition, PIM achieves enhanced dimensional parameter estimation accuracy with 6.9% reduction in maximum relative error; (2) in a moderate prior information condition, both models demonstrate comparable estimation performance; and (3) for a limited prior information condition, ODM exhibits superior parameter estimation capability showing 3.4% decrease in maximum relative error. …”
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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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Leveraging social science research to combat poverty and inequality in the Middle East - a pathway to achieving SDGs 1 and 10 by Chadi Azmeh, Hiba Darwich

“…The study analyzes data from 15 Middle Eastern countries from 2000 to 2023 using a panel regression model based on Panel-Corrected Standard Errors (PCSE) and Feasible Generalized Least Squares (FGLS) techniques. …”
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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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Investigation on the Role of Artificial Intelligence in Measurement System by P. A. Rezvy, Venkata Lakshmi Narayana Komanapalli

“…Soft computation using artificial neural networks and deep learning for linearization, compensation and error reduction, machine learning for estimation levaraging different algorithms like levenberg marquardt, scaled conjugate gradient, bayesian regularization, are assessed for training, testing and validation in real time and simulation. …”
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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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