Improving the quantification of peak concentrations for air quality sensors via data weighting by C. Frischmon, J. Silberstein, A. Guth, E. Mattson, J. Porter, M. Hannigan

“…When compared to unweighted colocation data, we demonstrate significant reductions in both error (root mean square error, RMSE) and bias (mean bias error, MBE) for pollutant peaks across all three datasets when data weighting is employed. …”
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Novel Spatio-Temporal Joint Learning-Based Intelligent Hollowing Detection in Dams for Low-Data Infrared Images by Lili Zhang, Zihan Jin, Yibo Wang, Ziyi Wang, Zeyu Duan, Taoran Qi, Rui Shi

“…Furthermore, it attained a sub-10% cross-sectional calculation error for hollowing dimensions, outperforming maximum entropy (70.5% error reduction) and OTSU (7.4% error reduction) methods, which shows our method being one novel method for automated intelligent hollowing detection.…”
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Using Adjoint-Based Forecast Sensitivity Method to Evaluate TAMDAR Data Impacts on Regional Forecasts by Xiaoyan Zhang, Hongli Wang, Xiang-Yu Huang, Feng Gao, Neil A. Jacobs

“…This study evaluates the impact of Tropospheric Airborne Meteorological Data Reporting (TAMDAR) observations on regional 24-hour forecast error reduction over the Continental United States (CONUS) domain using adjoint-based forecast sensitivity to observation (FSO) method as the diagnostic tool. …”
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Adaptive delta modulation method for arbitrary adaptation coefficients in the sensor network of the mobile ECG monitoring by О. В. Тимченко, В. І. Лозинський

“…However, the optimality of its value for reducing the quantization error of the adaptive delta encoder was not considered. …”
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Multi‐Satellite Data Assimilation for Large‐Scale Hydrological‐Hydrodynamic Prediction: Proof of Concept in the Amazon Basin by S. Wongchuig, R. Paiva, V. Siqueira, F. Papa, A. Fleischmann, S. Biancamaria, A. Paris, M. Parrens, A. Al Bitar

“…Assimilating WSE outperforms daily discharge and water‐level estimations, achieving 38% and 36% error reduction, respectively. However, the monthly evapotranspiration estimate achieves the greatest error reduction by assimilating SM with 11%. …”
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A roll attitude determination method based on the jamming energy of GEO satellites and an LSTM neural network by Rundong Li, Lu Feng, Peng Wu, Xiangling Deng, Haonan Shi

“…After real-time roll attitude testing of a rotating vehicle using different methods, the proposed method is found to be superior to the traditional least squares (LS) method, with a 48.97% reduction in the mean self-error and a 48.20% reduction in the mean Hall standardized error for the determined roll angles. …”
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A Study of Forced Vibrations with Nonlinear Springs and Dry Friction: Application to a Mechanical Oscillator with Very Large Vibrating Blades for Soil Cutting by Dario Friso

“…Measuring the maximum blade displacement compared with the calculated one provided a good accuracy of the mathematical modeling, resulting in a mean error of 5.6% and a maximum error of 7.2%.…”
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Integrating Copula-Based Random Forest and Deep Learning Approaches for Analyzing Heterogeneous Treatment Effects in Survival Analysis by Jong-Min Kim

“…Model performance is evaluated using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Concordance statistic (C-statistic), Average Treatment Effect (ATE), and Conditional Average Treatment Effect (CATE) by race. …”
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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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A balanced sensor scheduling for multitarget localization in a distributed multiple-input multiple-output radar network by Chenggang Wang, Zengfu Wang, Xiong Xu, Yuhang Hao

“…We formulate it as a minimum-variance estimation problem and show that the underlying variance reduction function is submodular. From the properties of submodularity, we present a balanced selection policy which minimizes the worst-case error value using a minimax strategy. …”
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Influence of Land Use Data Resolution on SCS Model in Runoff Simulation by WANG Menghan, HE Xiaohui, GUO Hengliang

“…Theresolution of land use data has a great influence on the CN value. The error ofmodel simulation can be reduced by applying land use data with higher resolution,with error reduction range between 5% and 10%.…”
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A Conceptual Investigation of a Large Radio Telescope Support Point Number Effect on Its Pointing Accuracy by Song Xue, Kaitong Ma, Congsi Wang, Peiyuan Lian, Yan Wang, Yuefei Yan, Qian Xu, Yuanpeng Zheng, Na Wang

“…Even though various compensation methods have been proposed to compensate for this pointing error, it remains as one of the challenges for the telescope pointing error reduction. …”
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EDUCATION, LABOUR PRODUCTIVITY AND INCOME INEQUALITY IN NIGERIA by SHARIMAKIN A., OSENI M.S., ADEGBOYE A.C.

“…The cointegration and error correction methodology is adopted in the empirical analysis. …”
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Simulation of Bimetallic Bush Hot Rolling Bonding Process by Yaqin Tian, Qingxue Huang

“…The simulation error is not more than 8%.…”
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Inherent thermodynamic performance assessment of a variable refrigerant flow system under transient cooling load: A case study of an eco-villa by Muhammad Reshaeel, Mohamed I. Hassan Ali

“…A mathematical model exhibiting a coefficient of determination of 0.89, a mean absolute error of 0.04, and a mean square error of 0.01, has been developed in MATLAB to evaluate system behavior. …”
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Improving prediction of solar radiation using Cheetah Optimizer and Random Forest. by Ibrahim Al-Shourbaji, Pramod H Kachare, Abdoh Jabbari, Raimund Kirner, Digambar Puri, Mostafa Mehanawi, Abdalla Alameen

“…The proposed CO-RF model outperforms others, achieving a low MAE of 0.0365, MSE of 0.0074, and an R2 of 0.9251 on the first dataset, and an MAE of 0.0469, MSE of 0.0032, and R2 of 0.9868 on the second dataset, demonstrating significant error reduction.…”
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A Non-Rigid Three-Dimensional Image Reconstruction Algorithm Based on Deformable Shape Reliability by Haiying Chen, Syed Atif Moqurrab

“…Our experimental results on 3D image sequence reconstructions demonstrate significant error reduction, underscoring our model’s credibility, robust reliability, and minimal re-projection error.…”
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Enhanced load frequency regulation in microgrids with renewable energy sources and electric vehicles by Imran Khan, Suheel Abdullah Malik, Amil Daraz, Baitullah Bareer

“…Simulation results show that the (I-P) cascaded PDN controller significantly outperforms existing controllers, such as PID, and PI-based models, by reducing frequency deviations, improving settling time, and minimizing overshoot and error indices. There is notable 77% reduction in overshoot (OSH) and 52% decrease in undershoot (USH) in tie-line power variations. …”
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The artificial intelligence-based agricultural field irrigation warning system using GA-BP neural network under smart agriculture. by Xiying Wang

“…The results reveal that as the number of nodes in the hidden layer increases, the model's Mean Squared Error (MSE) and Relative Error (RE) show a decreasing trend, indicating an improvement in model prediction accuracy. …”
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User Trajectory Prediction in Cellular Networks Using Multi-Step LSTM Approaches: Case Study and Performance Evaluation by Iskandar, Hajiar Yuliana, Hendrawan, Adriel Timoteo, Fabian Rafinanda Benyamin, Naufal Bhanu Anargyarahman

“…Performance evaluation is conducted using metrics like Mean Squared Error (MSE), Mean Absolute Error (MAE), and Average Distance Error to compare the models. …”
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