First M87 Event Horizon Telescope Results. III. Data Processing and Calibration by The Event Horizon Telescope Collaboration, Kazunori Akiyama, Antxon Alberdi, Walter Alef, Keiichi Asada, Rebecca Azulay, Anne-Kathrin Baczko, David Ball, Mislav Baloković, John Barrett, Dan Bintley, Lindy Blackburn, Wilfred Boland, Katherine L. Bouman, Geoffrey C. Bower, Michael Bremer, Christiaan D. Brinkerink, Roger Brissenden, Silke Britzen, Avery E. Broderick, Dominique Broguiere, Thomas Bronzwaer, Do-Young Byun, John E. Carlstrom, Andrew Chael, Chi-kwan Chan, Shami Chatterjee, Koushik Chatterjee, Ming-Tang Chen, Yongjun Chen, Ilje Cho, Pierre Christian, John E. Conway, James M. Cordes, Geoffrey B. Crew, Yuzhu Cui, Jordy Davelaar, Mariafelicia De Laurentis, Roger Deane, Jessica Dempsey, Gregory Desvignes, Jason Dexter, Sheperd S. Doeleman, Ralph P. Eatough, Heino Falcke, Vincent L. Fish, Ed Fomalont, Raquel Fraga-Encinas, Per Friberg, Christian M. Fromm, José L. Gómez, Peter Galison, Charles F. Gammie, Roberto García, Olivier Gentaz, Boris Georgiev, Ciriaco Goddi, Roman Gold, Minfeng Gu, Mark Gurwell, Kazuhiro Hada, Michael H. Hecht, Ronald Hesper, Luis C. Ho, Paul Ho, Mareki Honma, Chih-Wei L. Huang, Lei Huang, David H. Hughes, Shiro Ikeda, Makoto Inoue, Sara Issaoun, David J. James, Buell T. Jannuzi, Michael Janssen, Britton Jeter, Wu Jiang, Michael D. Johnson, Svetlana Jorstad, Taehyun Jung, Mansour Karami, Ramesh Karuppusamy, Tomohisa Kawashima, Garrett K. Keating, Mark Kettenis, Jae-Young Kim, Junhan Kim, Jongsoo Kim, Motoki Kino, Jun Yi Koay, Patrick M. Koch, Shoko Koyama, Michael Kramer, Carsten Kramer, Thomas P. Krichbaum, Cheng-Yu Kuo, Tod R. Lauer, Sang-Sung Lee, Yan-Rong Li, Zhiyuan Li, Michael Lindqvist, Kuo Liu, Elisabetta Liuzzo, Wen-Ping Lo, Andrei P. Lobanov, Laurent Loinard, Colin Lonsdale, Ru-Sen Lu, Nicholas R. MacDonald, Jirong Mao, Sera Markoff, Daniel P. Marrone, Alan P. Marscher, Iván Martí-Vidal, Satoki Matsushita, Lynn D. Matthews, Lia Medeiros, Karl M. Menten, Yosuke Mizuno, Izumi Mizuno, James M. Moran, Kotaro Moriyama, Monika Moscibrodzka, Cornelia Müller, Hiroshi Nagai, Neil M. Nagar, Masanori Nakamura, Ramesh Narayan, Gopal Narayanan, Iniyan Natarajan, Roberto Neri, Chunchong Ni, Aristeidis Noutsos, Hiroki Okino, Héctor Olivares, Gisela N. Ortiz-León, Tomoaki Oyama, Feryal Özel, Daniel C. M. Palumbo, Nimesh Patel, Ue-Li Pen, Dominic W. Pesce, Vincent Piétu, Richard Plambeck, Aleksandar PopStefanija, Oliver Porth, Ben Prather, Jorge A. Preciado-López, Dimitrios Psaltis, Hung-Yi Pu, Venkatessh Ramakrishnan, Ramprasad Rao, Mark G. Rawlings, Alexander W. Raymond, Luciano Rezzolla, Bart Ripperda, Freek Roelofs, Alan Rogers, Eduardo Ros, Mel Rose, Arash Roshanineshat, Helge Rottmann, Alan L. Roy, Chet Ruszczyk, Benjamin R. Ryan, Kazi L. J. Rygl, Salvador Sánchez, David Sánchez-Arguelles, Mahito Sasada, Tuomas Savolainen, F. Peter Schloerb, Karl-Friedrich Schuster, Lijing Shao, Zhiqiang Shen, Des Small, Bong Won Sohn, Jason SooHoo, Fumie Tazaki, Paul Tiede, Remo P. J. Tilanus, Michael Titus, Kenji Toma, Pablo Torne, Tyler Trent, Sascha Trippe, Shuichiro Tsuda, Ilse van Bemmel, Huib Jan van Langevelde, Daniel R. van Rossum, Jan Wagner, John Wardle, Jonathan Weintroub, Norbert Wex, Robert Wharton, Maciek Wielgus, George N. Wong, Qingwen Wu, André Young, Ken Young, Ziri Younsi, Feng Yuan, Ye-Fei Yuan, J. Anton Zensus, Guangyao Zhao, Shan-Shan Zhao, Ziyan Zhu, Roger Cappallo, Joseph R. Farah, Thomas W. Folkers, Zheng Meyer-Zhao, Daniel Michalik, Andrew Nadolski, Hiroaki Nishioka, Nicolas Pradel, Rurik A. Primiani, Kamal Souccar, Laura Vertatschitsch, Paul Yamaguchi

“…We present the calibration and reduction of Event Horizon Telescope (EHT) 1.3 mm radio wavelength observations of the supermassive black hole candidate at the center of the radio galaxy M87 and the quasar 3C 279, taken during the 2017 April 5–11 observing campaign. …”
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The Effect of Stress Distribution on Tibial Implants with a Honeycomb Structure in Open-Wedge High Tibial Osteotomy by Zengbo Xu, Chunhui Mu, Yi Xia

“…The biomechanical experimental results of experiments on tibial implants exhibit similar mechanical response patterns to the established finite element model, whose maximum displacement error is 1.18% under 1500 N compressive load. The hybrid porous implant developed in this study demonstrated significant stress reductions in both tibial bone (19.97% and 15.33% lower than mono-porous configurations at 73% porosity) and implant body (31.60% and 11.83% reductions, respectively), while exhibiting diminished micromotion tendencies. …”
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A hybrid deep learning framework for global irradiance prediction using fuzzy C-Means, CNN-WNN, and Informer models by Walid Mchara, Lazhar Manai, Mohamed Abdellatif Khalfa, Monia Raissi, Wissem Dimassi, Salah Hannachi

“…The proposed CNN-WNN-Informer model achieved average reductions across all cities of 67.7% in t-statistic, 73.9% in Mean Absolute Percentage Error (MAPE), 82.5% in Mean Absolute Bias Error (MABE), and 59.0% in Root Mean Square Error (RMSE), underscoring its significant improvements. …”
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A novel multimodal image feature fusion mechanism: Application to rabbit liveweight estimation in commercial farms by Daoyi Song, Zhenhao Lai, Shuqi Yang, Dongyu Liu, Jinxia (Fiona) Yao, Hongying Wang, Liangju Wang

“…The network was trained and validated on a dataset of 1,957 overhead images from 300 rabbits collected from a commercial farm, achieving superior performance with an R-Square (R2) of 0.95, Root Mean Square Error (RMSE) of 194.95 g, Mean Absolute Deviation (MAD) of 155.10 g, Mean Absolute Percentage Error (MAPE) of 4.08 %, and Mean Coefficient of Variation (MCV) of 3.03 %. …”
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Multistation Wind Speed Forecasting Based on Dynamic Spatiotemporal Graph Convolutional Networks by Jianhong Gan, Runqing Kang, Xun Deng, Chentao Mao, Zhibin Li, Peiyang Wei, Chunjiang Wu, Tongli He

“…Experimental results demonstrate that the DSTGFP model achieves reductions of 24.66% in the mean absolute error and 25.47% in the root-mean-square error compared to existing deep learning methods, highlighting its superior predictive performance.…”
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Synergistic SAPSO-sinusoidal decay empirical formula for ship motion forecasting in waves by Jianwei Wang, Xinyu Han, Jiachen Chai, Wenlei Li, Ze He, Minghua Yue

“…Comparative analysis reveals that SAPSO achieves significant performance enhancements over standard PSO, with mean reductions of 70.8% in Mean Squared Error (MSE) and 40.1% in Root Mean Squared Error (RMSE), indicating substantially improved convergence stability. …”
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Urban Signalized Intersection Traffic State Prediction: A Spatial–Temporal Graph Model Integrating the Cell Transmission Model and Transformer by Anran Li, Zhenlin Xu, Wenhao Li, Yanyan Chen, Yuyan Pan

“…Validation using real traffic data from pNEUMA demonstrates that CeT significantly outperforms baseline models in two-phase signalized intersection scenarios, achieving reductions of 11.47% in Mean Absolute Error (MAE), 13.48% in Root Mean Square Error (RMSE), and an increase of 4.36% in Accuracy (ACC). …”
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Power Assessment and Performance Comparison of Wind Turbines Driven by Multivariate Environmental Factors by Bubin Wang, Bin Zhou, Denghao Zhu, Mingheng Zou, Zhao Rao, Haoxuan Luo, Weihao Ji

“…The proposed method achieves substantial improvements in predictive accuracy, with decreases of 9.39% in mean absolute error (MAE) and 11.75% in root mean square error (RMSE), compared to conventional binning approaches. …”
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Research on Wafer CMP Temperature Online Detection Compensation Algorithm Based on GA-BP Improved Neural Network by Binjie Li, Kuan Shen, Zhilong Song, Binghai Lyu, Wenhong Zhao

“…Experimental results indicate that the mean absolute error (MAE) of the test set has decreased to 0.2381°C, representing reductions of 82% and 68% compared to BP and GA-BP, respectively. …”
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Brief communication: Improving lake ice modeling in ORCHIDEE-FLake model using MODIS albedo data by Z. Titus, Z. Titus, A. Cuynet, E. Salmon, C. Ottlé

“…The results are in better agreement with the observations for all lake size categories, with the largest and deepest lakes showing more significant error reductions in the duration of the ice cover period up to 18 d. …”
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Enhanced Seafloor Topography Inversion Using an Attention Channel 1D Convolutional Network Based on Multiparameter Gravity Data: Case Study of the Mariana Trench by Qiang Wang, Ziyin Wu, Zhaocai Wu, Mingwei Wang, Dineng Zhao, Taoyong Jin, Qile Zhao, Xiaoming Qin, Yang Liu, Yifan Jiang, Puchen Zhao, Ning Zhang

“…Results of a case study of the Mariana Trench indicated that the AC1D grid predictions exhibited improved agreement with single-beam depth checkpoints, with standard deviation reductions of 6.32%, 20.79%, and 36.77% and root mean square error reductions of 7.11%, 22.82%, and 50.99% compared with those of parallel linked backpropagation, the gravity–geological method, and a convolutional neural network, respectively. …”
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Modeling the Effects of Vegetation on Air Purification Through Computational Fluid Dynamics in Different Neighborhoods of Beijing by Bin Cai, Haomiao Cheng, Fanding Xiang, Han Wang, Tianfang Kang

“…The simulation results were also well validated by the trial-and-error method compared with the computation of vegetation absorption coefficients. …”
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A dual-path convolutional neural network combined with an attention-based bidirectional long short-term memory network for stock price prediction. by Guiyan Zhao, Yunfei Cheng, Jianhui Yang, Jiayuan Ouyang

“…The DCA-BiLSTM achieves an [Formula: see text] of 0.9507 for Apple, 0.9595 for Google, 0.9077 for Tesla, and 0.9594 for the Nasdaq index, with significant reductions in error metrics across all datasets. These results demonstrate the model's robustness and improved predictive accuracy, offering reliable insights for stock price forecasting.…”
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Machine learning-based pseudo-continuous pedotransfer function for predicting soil freezing characteristic curve by Sangyeong Park, Yongjoon Choe, Hangseok Choi, Khanh Pham

“…The performance of the XGB-PTF was rigorously evaluated and compared with two high-performance empirical models. Significant reductions in root mean square error and mean absolute error of 42% and 55%, respectively, demonstrated the superiority of the XGB-PTF. …”
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Research on time series prediction model for multi-factor environmental parameters in facilities based on LSTM-AT-DP model by Longwei Liang, Longwei Liang, Hui Shi, Hui Shi, Zhaoyuan Wang, Shengjie Wang, Changhong Li, Ming Diao

“…Corresponding RMSE reductions were 0.6830, 1.8759, and 12.952 for these parameters.DiscussionThe results demonstrate that the LSTM-AT-DP model significantly enhances prediction accuracy while effectively suppressing error accumulation in long-term forecasts. …”
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Application of artificial neural networks for predicting soil settlement in geotechnical applications with plastic waste reinforcement above buried pipes by Sinan A. Al-Haddad, Hala Adnan Abbas, Luttfi A. Al-Haddad, Mustafa I. Al-Karkhi

“…The results demonstrate significant reductions in settlement with plastic waste reinforcement, with mattress depth to width of the loading steel plate reinforcement ratios u/B = 0.5, u/B = 1.0, and u/B = 1.5 exhibiting settlement reductions of 0.25 mm, 2.3 mm, and 4.5 mm, respectively, compared to the unreinforced condition. …”
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A Multi-Vector Modulated Model Predictive Control Based on Coordinated Control Strategy of a Photovoltaic-Storage Three-Port DC–DC Converter by Qihui Feng, Meng Zhang, Yutao Xu, Chao Zhang, Dunhui Chen, Xufeng Yuan

“…The proposed modulated MPC method utilizes three basic vectors to calculate the optimal switching sequence for minimizing the error vector. It can significantly minimize voltage ripple while maintaining the nonlinear and dynamic performance characteristics of a traditional MPC. …”
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Distributed Adaptive Coding Optimization for IoT Using Fulcrum Code and Model-Agnostic Meta-Learning (MAML) in Ultra-Low Latency Environments by Yair Rivera Julio, Angel Pinto, Rodrigo Garcia, Jose Aguilar, Nelson A. Perez-Garcia

“…FEC and HARQ further balance error correction and retransmission overhead. Simulations show significant reductions in transmission time and energy consumption, particularly in high-packet-loss scenarios. …”
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Enhancing Precipitation Nowcasting Through Dual-Attention RNN: Integrating Satellite Infrared and Radar VIL Data by Hao Wang, Rong Yang, Jianxin He, Qiangyu Zeng, Taisong Xiong, Zhihao Liu, Hongfei Jin

“…Notably, the DA-TrajGRU model achieves reductions in mean squared error (MSE) and mean absolute error (MAE) of 30 (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>9.3</mn><mo>%</mo></mrow></semantics></math></inline-formula>) and 89 (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>6.4</mn><mo>%</mo></mrow></semantics></math></inline-formula>), respectively, compared with those of the conventional TrajGRU model. …”
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Flight Trajectory Prediction Based on Automatic Dependent Surveillance-Broadcast Data Fusion with Interacting Multiple Model and Informer Framework by Fan Li, Xuezhi Xu, Rihan Wang, Mingyuan Ma, Zijing Dong

“…The results demonstrate that the IMM-Informer framework has notable prediction error reductions and significantly outperforms the prediction accuracies of the standalone sequence prediction network models.…”
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