An Improved Tropospheric Tomographic Model Based on Artificial Neural Network by Minghao Zhang, Kefei Zhang, Suqin Wu, Longjiang Li, Dantong Zhu, Moufeng Wan, Peng Sun, Jiaqi Shi, Shangyi Liu, Andong Hu

“…Statistical results show that the root mean square error (RMSE) of slant water vapor reconstructed from the improved model is reduced to 1.35 from 2.85 mm of the traditional model. …”
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FEN-MRMGCN: A Frontend-Enhanced Network Based on Multi-Relational Modeling GCN for Bus Arrival Time Prediction by Ting Qiu, Chan-Tong Lam, Bowie Liu, Benjamin K. Ng, Xiaochen Yuan, Sio Kei Im

“…Experimental results demonstrate that our frontend-enhanced network achieves a reduction in the Mean Absolute Percentage Error (MAPE) by 15.36%, 13.44%, and 19.07% compared to traditional time series forecasting models like CNN, LSTM, and Transformer, respectively. …”
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Emerging role of generative AI in renewable energy forecasting and system optimization by Erdiwansyah, Rizalman Mamat, Syafrizal, Mohd Fairusham Ghazali, Firdaus Basrawi, S.M. Rosdi

“…Results indicate that GAN-based models reduce root mean square error (RMSE) by 15–20 % in solar irradiance forecasting and significantly enhance spatial-temporal wind simulations. …”
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Harnessing solar drying for starter cultures: A novel approach to backslopping fermentation by Marcel Houngbédji, Donan Bangbadé, D. Sylvain Dabadé, Schadrac D. Agossevi, B. Pélagie Agbobatinkpo, S. Wilfrid Padonou, Joseph Dossou, Paulin Azokpota, D. Joseph Hounhouigan

“…The drying kinetic patterns showed consistent fit with Midili-Kucuk model with high reliable predictivity (R2 = 0.998) and least bias (Root Mean Square Error = 0.016), demonstrating the suitability of solar drying for small-scale production of starter culture. …”
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Safety and efficacy of simultaneous photorefractive keratectomy and corneal cross-linking in managing suspected keratoconus by Ali Dal, Mehmet Canleblebici, Murat Erdağ

“…Eligibility criteria included stable refractive error for at least 1 year, spherical equivalent refractive error not exceeding −4.0 D, and central corneal thickness between 470 and 500 µm. …”
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The optimization path of agricultural industry structure and intelligent transformation by deep learning by Xingchen Pan, Jinyu Chen

“…In crop yield prediction, the proposed method achieves superior performance, as evidenced by reductions in both absolute error and mean squared error, along with attaining the highest R2 value (0.93). …”
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Vector Decimation Harmonic Mean-Based Algorithm for Online Acoustic Feedback Active Noise Control by Suman Turpati, Ajay Roy, Tathababu Addepalli, Mohammed Alkahtani, Abdullatif Hakami, Ahmad Faiz Minai, Abdallah Hammad

“…It is achieved by decimating the filter weight vector into small vectors and then updating each vector using the harmonic mean error of the VSS FxLMS. To ensure the enhanced effectiveness of the proposed solution, the AF-FxLMS, online-AFFNFxLMS, and HM-AFFNFxLMS algorithms are used to update the filter weights as a demonstration. …”
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Revolutionizing Clear-Sky Humidity Profile Retrieval with Multi-Angle-Aware Networks for Ground-Based Microwave Radiometers by Yinshan Yang, Zhanqing Li, Jianping Guo, Yuying Wang, Hao Wu, Yi Shang, Ye Wang, Langfeng Zhu, Xing Yan

“…Based on the 7-year (2018–2024) in situ measurements from Beijing, Nanjing, and Shanghai, validation results reveal that AngleNet achieves substantial improvements, with an average R2 of 0.71 and a root mean square error (RMSE) of 10.39%, surpassing conventional models such as LGBM (light gradient boosting machine) and RF (random forest) by over 10% in both metrics, and demonstrating a remarkable 41% increase in R2 and a 10% reduction in RMSE compared to the previous BRNN method (batch normalization and robust neural network). …”
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Deep Learning for Opportunistic Rain Estimation via Satellite Microwave Links by Giovanni Scognamiglio, Andrea Rucci, Attilio Vaccaro, Elisa Adirosi, Fabiola Sapienza, Filippo Giannetti, Giacomo Bacci, Sabina Angeloni, Luca Baldini, Giacomo Roversi, Alberto Ortolani, Andrea Antonini, Samantha Melani

“…Compared to the state-of-the-art power-law-based models applied to similar datasets reported in the literature, our ML models achieve a <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>46</mn><mo>%</mo></mrow></semantics></math></inline-formula> reduction in the root mean squared error (RMSE) for event-based cumulative precipitation predictions.…”
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Research on non-destructive testing method for radial defect of anchor bolt based on longitudinal guided wave by Deying TANG, Weimin TANG, Zhiyi ZENG, Qing TIAN, Ailing LI, Yaling GUO, Erdi A’BI, Tongyu WU, Yibo WU

“…The radial defects of anchor bolt caused by long-term corrosion lead to the reduction of its bearing capacity, which led to a large number of engineering accidents. …”
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Modeling drug retention as memory effects in obese patients using fractional and augmented models by Amani R. Ynineb, Erhan Yumuk, Dana Copot, Bora Ayvaz, Bouchra Khoumeri, Ghada Ben Othman, Marcian D. Mihai, Isabela R. Birs, Cristina I. Muresan, Clara M. Ionescu

“…During the awakening phase, both the augmented and fractional-order models reduce BIS prediction error compared to the classical model. The augmented model lowers RMSE by 22.5% (from 10.38 to 8.04), while the fractional model achieves a 21.4% reduction (to 8.16) (based on one obese patient case). …”
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Dynamic Shock-Transmission Mechanism Between U.S. Trade Policy Uncertainty and Sharia-Compliant Stock Market Volatility of GCC Economies by Mosab I. Tabash, Suzan Sameer Issa, Marwan Mansour, Mohammed W. A. Saleh, Maha Rahrouh, Kholoud AlQeisi, Mujeeb Saif Mohsen Al-Absy

“…Conversely, Qatar and Kuwait show the least transmission of error variance from TPU during higher-volatility conditions (<i>τ</i> = 0.95). …”
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High-precision large-aperture single-frame interferometric surface profile measurement method based on deep learning by Liang Tang, Mingzhi Han, Shuai Yang, Ye Sun, Lirong Qiu, Weiqian Zhao

“…The experimental results show that for the tested mirror with Φ = 820 mm, the surface profile obtained from LA-SFISPM is subtracted point-by-point from the ground truth, resulting in a maximum single-point error of 4.56 nm. Meanwhile, the peak-to-valley (PV) value is 0.075 8 λ, and the simple repeatability of root mean square (SR-RMS) value is 0.000 25 λ, which aligns well with the measured results obtained by ZYGO. …”
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A comprehensive IoT cloud-based wind station ready for real-time measurements and artificial intelligence integration by Décio Alves, Fábio Mendonça, Sheikh Shanawaz Mostafa, Fernando Morgado-Dias

“…Calibration procedures reduced the maximum mean error of the ultrasonic wind sensors from 0.7 m/s to 0.2 m/s, achieving a 71% improvement in measurement accuracy. …”
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Patient-specific fluid–structure simulations of anomalous aortic origin of right coronary arteriesCentral MessagePerspective by Michael X. Jiang, MD, MEng, Muhammad O. Khan, PhD, Joanna Ghobrial, MD, Ian S. Rogers, MD, Gosta B. Pettersson, MD, PhD, Eugene H. Blackstone, MD, Alison L. Marsden, PhD

“…After we tuned the distal coronary resistances to achieve a stress flow rate triple that at rest, the simulations adequately matched the measured iFRs (r = 0.85, root-mean-square error = 0.04). The intramural lumen remained narrowed with simulated stress and resulted in lower iFRs without needing external compression from the pulmonary root. …”
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Comparative evaluation of accuracy, precision, and radiation dose between mindways and low-dose iCare QCT for lumbar spine BMD using the European spine phantom by Yujie Li, Yujie Li, Tiantian Yin, Tiantian Yin, Qiushi Yang, Heli Han, Zeguo Wang, Wanjiang Yu

“…Accuracy was evaluated using relative measurement error (RME), and precision was assessed via relative standard deviation (RSD). …”
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Mechanical Anastomotic Coupling Device versus Hand-sewn Venous Anastomosis in Head and Neck Reconstruction—An Analysis of 1694 Venous Anastomoses by Rajan Arora, Kripa Shanker Mishra, Hemant T. Bhoye, Ajay Kumar Dewan, Ravi K. Singh, Ravikiran Naalla

“…The venous anastomosis is a critical step in free-tissue transfer. The margin of error is less and the outcome depends on the surgeon’s skill and technique. …”
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Development of a 3D-Printed Chest Phantom with Simulation of Lung Nodules for Studying Ultra-Low-Dose Computed Tomography Protocols by Jenna Silberstein, Steven Tran, Yin How Wong, Chai Hong Yeong, Zhonghua Sun

“…An optimal protocol of 80 kVp and 30 mAs was identified for lung nodule detection, delivering a dose of only 0.23 mSv, which represents a 96% reduction compared to standard CT protocols. The measurement error between patient and phantom scans was −0.03 ± 0.14 cm. …”
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Impacts of Spatial Expansion of Urban and Rural Construction on Typhoon-Directed Economic Losses: Should Land Use Data Be Included in the Assessment? by Siyi Zhou, Zikai Zhao, Jiayue Hu, Fengbao Liu, Kunyuan Zheng

“…Results demonstrate three key findings: (1) By introducing prototype learning, a meta-learning approach, to guide model updates, we achieved precise assessments with small training samples, attaining an MAE of 1.02, representing 58.5–76.1% error reduction compared to conventional machine learning algorithms. …”
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A Method to Estimate Sea Surface Wind Vectors Using Geostationary Satellites by Zhang Yunkai, Xu Na, Zhai Xiaochun, Zhang Peng

“…This represents a reduction of up to 0.24 m·s-1 compared to the empirical model. …”
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