Atropine (0.05%) for rapid progressive childhood myopia (ARM study) by Rohit Saxena, Vinay Gupta, Himani Thakur, Rebika Dhiman, Thirumurthy Velpandian, Swati Phuljhele, Namrata Sharma

“…The study observed the response to treatment was independent of the age at baseline, baseline refractive error, baseline rate of progression, gender, and family history of myopia. …”
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Chemometric and computational modeling of polysaccharide coated drugs for colonic drug delivery by Ahmad Khaleel AlOmari, Khaled Almansour

“…Assessment metrics, such as the coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE), emphasize the MLP model’s exceptional performance. …”
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Machine learning techniques in monitoring and controlling friction stir welding process: a critical review by Bhardwaj Kulkarni, Saurabh Tayde, Yashwant Chapke, Swapnil Vyavahare, Avinash Badadhe

“…By employing machine learning techniques, the FSW process can become more cost-effective through optimizing process parameters, early detection of defects and tool failures, reduction of waste, and attainment of superior joint properties, all while minimizing the need for extensive trial and error. …”
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Research on channel estimation based on joint perception and deep enhancement learning in complex communication scenarios by Xin Liu, Shanghong Zhao, Yanxia Liang, Shahid Karim

“…Experimental results demonstrate that the framework achieves significant channel estimation accuracy and robustness across several public datasets and real test scenarios, with the channel estimation error markedly smaller than that of traditional least squares (LS) and linear minimum mean square error (LMMSE) methods. …”
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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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Uncertainty-Guided Prediction Horizon of Phase-Resolved Ocean Wave Forecasting Under Data Sparsity: Experimental and Numerical Evaluation by Yuksel Rudy Alkarem, Kimberly Huguenard, Richard W. Kimball, Stephan T. Grilli

“…Results show under a 50% probability of upstream data loss, the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>τ</mi></semantics></math></inline-formula>-trimmed TiDE model achieves a 46% reduction in error at the most upstream target, compared to 22% for LSTM. …”
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Methodology for Determining the Effective Thickness of the Cemented Layer of Steel by S. G. Sandomirski, A. L. Val’ko, S. P. Rudenko

“…The technique provides a significant reduction in the influence of the structural banding of the metal and the inevitable error in measuring hardness on the result of determining the hef . …”
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Experimental Study on The Mechanical Properties of Novel Viscous Mass Damper by CAI Yijie, XU Weizhi, WANG Shuguang, DU Dongsheng, SUN Yue

“…In terms of force magnitude, the maximum error was 42.5 kN, while the average error was as low as 20.97 kN. …”
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High-resolution population mapping based on SDGSAT-1 glimmer imagery and deep learning: a case study of the Guangdong-Hong Kong-Macao Greater Bay Area by Haoxuan Duan, Zhongqi Shi, Ji Ge, Fan Wu, Yuzhou Liu, Hong Zhang, Chao Wang

“…It also outperformed other population spatialization datasets and NTL data by over 30% and 10%, respectively, in terms of error reduction. The results highlight the method’s effectiveness and the value of SDGSAT-1 glimmer imagery for fine population spatialization.…”
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An Effective Hybrid Strategy: Multi-Fuzzy Genetic Tracking Controller for an Autonomous Delivery Van by Mohammad Ghazali, Zaid Samadi, Mehmet Gol, Ali Demir, Kemal Rodoplu, Tarek Kabbani, Emrecan Hatipoğlu, Ahu E. Hartavi

“…The results show that the proposed strategy leads to a reduction of up to 91.2% and 61.1% in tracking error compared to the manually and geometrically weighted alternatives, respectively.…”
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A Self-Compensating Non-Intrusive Ring-Type AC Voltage Sensor Based on Capacitive Coupling by Junpeng Wang, Jiacheng Li, Chunrong Peng, Zhengwei Wu, Dengfeng Ju, Qiang Zhang

“…The effects of changes in cable diameter and cable position on the measurement were tested separately. The worst-case error of the sensor output is less than 6.44%, representing a reduction of 21.4% compared to the uncompensated case. …”
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Machine-learning based high-bandwidth magnetic sensing by Galya Haim, Stefano Martina, John Howell, Nir Bar-Gill, Filippo Caruso

“…Our results indicate a potential reduction of required data points by at least a factor of 3, while maintaining the current error level. …”
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An Optimal Approach for Assessing Weibull Parameters and Wind Power Potential for Six Coastal Cities in Pakistan by Ghulam Abbas, Arshad Ali, Faizan Rashid, Naveed Ashraf, Zohaib Mushtaq, Muhammad Zubair

“…An enormous reduction in wind power density-based percentage error (for example, 15.3531% than 51.7205% for Gwadar at 10 m height) was observed in NEPFM-SSA compared to NEPFM. …”
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Safety Status Prediction Model of Transmission Tower Based on Improved Coati Optimization-Based Support Vector Machine by Xinxi Gong, Yaozhong Zhu, Yanhai Wang, Enyang Li, Yuhao Zhang, Zilong Zhang

“…The predictive outcomes indicate that the proposed ICOA-SVM model exhibits rapid convergence and high prediction accuracy, with a 62.5% reduction in root mean square error, a 59.6% decrease in average relative error, and a 75.0% decline in average absolute error compared to the conventional support vector machine. …”
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The Correlation of Microscopic Particle Components and Prediction of the Compressive Strength of Fly-Ash-Based Bubble Lightweight Soil by Yaqiang Shi, Hao Li, Hongzhao Li, Zhiming Yuan, Wenjun Zhang, Like Niu, Xu Zhang

“…The Bayesian-optimized Random Forest model performed optimally in terms of the mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE), and the prediction performance was best. …”
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Crushing Force Prediction Method of Controlled-Release Fertilizer Based on Particle Phenotype by Linlin Sun, Xiubo Chen, Zixu Chen, Linlong Jing, Jinxing Wang, Xinpeng Cao, Shenghui Fu, Yuanmao Jiang, Hongjian Zhang

“…Comparative tests with a random forest regression, the K-nearest neighbor, a back propagation (BP) neural network, and a long short-term memory (LSTM) neural network have demonstrated that the PSO-SVM model outperforms these methods in terms of mean absolute error, root mean square error, and correlation coefficient, underscoring its effectiveness. …”
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Hardware-Centric Exploration of the Discrete Design Space in Transformer–LSTM Models for Wind Speed Prediction on Memory-Constrained Devices by Laeeq Aslam, Runmin Zou, Ebrahim Shahzad Awan, Sayyed Shahid Hussain, Kashish Ara Shakil, Mudasir Ahmad Wani, Muhammad Asim

“…A contribution of this research is the development of a novel cost function that balances the reduction of mean squared error with the constraints of model size. …”
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Pressure-Related Discrepancies in Landsat 8 Level 2 Collection 2 Surface Reflectance Products and Their Correction by Santosh Adhikari, Larry Leigh, Dinithi Siriwardana Pathiranage

“…For the green band, the reduction in error was much less due to the significantly lesser impact of aerosol on this band. …”
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