Evaluation of the influence of hydrodynamic cavitation treatment of dark petroleum products on the yield of fractions with boiling points up to 400°C by B. V. Peshnev, E. V. Burlyaeva, V. B. Terenteva, D. V. Nikishin, A. I. Nikolaev, K. S. Andronov

“…Objectives. The reduction of the anthropogenic burden on the environment is generally associated with the transition to alternative energy sources. …”
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Prediction of moisture content of hummus peach based on multi-burr hyperspectral data by GAO Aidi, QIAO Fengzhang, ZHU Wenxuan, ZHONG Xiaopin, DENG Yuanlong

“…The coefficient of determination (<i>R</i>²) was 0.928, and the root mean square error (RMSE) was 0.008 4 on the training set. The <i>R</i>² was 0.926, and the RMSE was 0.009 2 on the testing set. …”
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Dynamic Simulation of Ground Braking Force Control Based on Fuzzy Adaptive PID for Integrated ABS-RBS System with Slip Ratio Consideration by Pinjia Shi, Yongjun Min, Hui Wang, Liya Lv

“…The proposed control strategy achieves remarkable precision improvements, reducing integral time absolute error (<i>ITAE</i>) by 8.3–52.8% compared to conventional methods. …”
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Deep learning-based thermal modeling of slipping velocity in oblique magnetohydrodynamic non-Newtonian nanofluid flows by Nek Muhammad Katbar, Hina Zahir, Kamel Guedri, Basim M. Makhdoum, Siti Suzilliana Putri Mohamed Isa, Syed M. Hussain, Abbas Khan

“…Statistical comparisons confirm the accuracy of ANN with a mean square error below 10^-5. The results reveal that an increase in the homogeneous reaction parameter enhances nanoparticle dispersion, thereby increasing the nanofluid temperature by approximately 18.6 % for the shear-thinning case and 22.3 % for the shear-thickening case. …”
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Research on Newton-Krylov Method for Multi-component Flow Heat Transfer Coupled System of High-temperature Gas-cooled Reactor by TANG Huanran, ZHANG Han, LIU Lixun, PENG Xinru, WU Yingjie, GUO Jiong, LI Fu

“…The correctness of this program was verified by comparison with the high-temperature gas-cooled reactor analysis program TINTE. The maximum error between this program and TINTE is less than 0.1%. …”

On the parametric analytics to investigate hole quality characteristics during helical milling of carbon fiber reinforced plastics stacks by Mehdi Tlija, Nimra Naeem, Mohammad Pervez Mughal, Kiran Mughal, Saad Ullah, Muhammad Sana, Anamta Khan

“…Analysis of variance is used to study the impact of input variables on the hole quality aspects that include delamination damage, length of uncut fiber, circularity error, and surface roughness by using the Taguchi design of experiment. …”
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Research on dynamic prediction and optimization of high altitude photovoltaic power generation efficiency using GVSAO-CNN Model under 8-climate modes by Xiaoming Xiong, Heng Hu, Qiangfu Jia, Rongjian Zhang, Chongan Huang, Qingyuan Lu

“…Specifically, it has been demonstrated to achieve a 22.6% reduction in root-mean-square error (RMSE) and a 16.7% decrease in training time when compared to traditional models. …”
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Postoperative Apnea‐Hypopnea Index Prediction of Velopharyngeal Surgery Based on Machine Learning by Jingyuan You, Juan Li, Yingqian Zhou, Xin Cao, Chunmei Zhao, Yuhuan Zhang, Jingying Ye

“…Surgical success was defined as a ≥50% reduction in AHI to a final AHI of <20 events/h. Results A total of 152 OSA adult patients (median [interquartile range] age = 40 [35, 48] years, male/female = 136/16) were included in this study. …”
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Estimation of the air conditioning energy consumption of a classroom using machine learning in a tropical climate by Liliana Ortega-Diaz, Julian Jaramillo-Ibarra, German Osma-Pinto

“…Air conditioning energy consumption in buildings represents a considerable percentage of total energy consumption, which underlines the importance of implementing measures contributing to its reduction. Predicting energy consumption is critical to making informed decisions and identifying factors influencing power consumption. …”
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Year patterns of climate impact models' performance: Long-term simulation of rainfed spring wheat production using five crop models under various climate patterns by Funian Zhao, Qiang Zhang, Heling Wang, Kai Zhang

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Trend and prediction of daily incidence of hand, foot, and mouth disease in Shenzhen, 2011 - 2023 with projections to 2024: a Prophet model approach by Wenhai LU, Lixia SONG, Huawei XIONG, Zhigao CHEN, Yan LU, Yanpeng CHENG, Zhen ZHANG

“…Model performance was evaluated using four metrics: mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE), and symmetric mean absolute percentage error (SMAPE). …”
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Prediction of Carbonate Reservoir Porosity Based on CNN-BiLSTM-Transformer by Yingqiang Qi, Shuiliang Luo, Song Tang, Jifu Ruan, Da Gao, Qianqian Liu, Sheng Li

“…This model is applied to the Moxi gas field in the Sichuan Basin, using conventional logging curves as input feature variables for porosity prediction. Root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R²) are used as evaluation metrics for comprehensive analysis and comparison. …”
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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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Sidewalk thermal comfort in street canyon and role of pavements under perpendicular sun rays during summer by D. Jareemit, M. Srivanit

“…Field data validation showed acceptable errors (R² = 0.91, normalized mean square error = 0.17).FINDINGS: For an east-west-oriented deep street canyon with direct overhead sunlight in summer, low-albedo materials for road and sidewalk surfaces are recommended to enhance thermal comfort. …”
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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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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

“…Conclusions: Patient-specific FSI modeling of AAORCA is a promising, noninvasive method to assess the iFR reduction caused by intramural geometries and inform surgical intervention. …”
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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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A Novel Data-Driven MPC Framework Using KDE and KPCA for Autonomous Vehicles by Romdhane Nasri, Jannet Jamii, Majdi Mansouri, Zouhaier Affi, Vicenc Puig

“…Extensive experimental validation on an autonomous vehicle platform yields outstanding performance metrics: lateral tracking errors of 0.0225 m (RMSE), heading errors of 0.0476 rad, and longitudinal velocity tracking errors of 0.2725 m/s. …”
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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

“…The proposed method has superior noise reduction effectiveness compared to other algorithms, which indiscriminately attenuate the control filter across all frequencies. …”
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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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