Accurate hourly AQI prediction using temporal CNN-LSTM-MHA+GRU: A case study of seasonal variations and pollution extremes in Visakhapatnam, India by Sreenivasulu T, Mokesh Rayalu G

“…The framework amalgamates one-dimensional Convolutional Neural Networks (1D CNN) for the extraction of short-term patterns, Long Short-Term Memory (LSTM) integrated with Multi-Head Attention (MHA) to encapsulate long-term dependencies, and Gated Recurrent Unit (GRU) for the refinement of residual errors. The model, trained on a dataset comprising 14,073 records and optimized through Bayesian parameter tuning, demonstrated robust performance on the test dataset (R² = 0.9757, RMSE = 6.29, MAPE = 7.07 %). …”
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The Effectiveness of Electronic Differential Diagnoses (DDX) Generators: A Systematic Review and Meta-Analysis. by Nicholas Riches, Maria Panagioti, Rahul Alam, Sudeh Cheraghi-Sohi, Stephen Campbell, Aneez Esmail, Peter Bower

“…<h4>Background</h4>Diagnostic errors are costly and they can contribute to adverse patient outcomes, including avoidable deaths. …”
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Bayesian optimization with Gaussian-process-based active machine learning for improvement of geometric accuracy in projection multi-photon 3D printing by Jason E. Johnson, Ishat Raihan Jamil, Liang Pan, Guang Lin, Xianfan Xu

“…In each case, the active learning framework improves the geometric accuracy, with drastic reductions of the errors to within the measurement accuracy in just four iterations of the Bayesian optimization using only a few hundred of total training data. …”
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Performance analysis of dual-fuel engines using acetylene and microalgae biodiesel: The role of fuel injection timing by M. Sonachalam, R. Jayaprakash, V. Manieniyan, P.S. Raghavendra Rao, G. Vinodhini, Manish Sharma, Teku Kalyani, Mahammadsalman Warimani, Hasan Sh Majdi, T.M. Yunus Khan, Abdul Saddique Shaik, Keerthi Shetty

“…To predict engine performance and emission characteristics, advanced machine learning models were employed and evaluated using four statistical criteria, including R-squared, mean absolute error (MAE), and mean squared error (MSE). Experimental results indicated that the optimal configuration involved a dual-fuel mode combining B20 MEOA with acetylene gas and an advanced FIT of 25° before top dead center (bTDC). …”
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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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Liquid–liquid–solid three-phase flow in horizontal wells: flow pattern modeling and dimensionless analysis by Hamed Nejati, Saeed Shad, Sasan Yousefirad, Aras Sheikhi

“…Finally, a mathematical model is proposed to predict the flow regime boundaries from stationary bed to accumulative bed flow regimes with an error of less than 5%.…”
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The CoDiNOS trial protocol: an international randomised controlled trial of intravenous sildenafil versus inhaled nitric oxide for the treatment of pulmonary hypertension in neonat... by Anne Greenough, Thomas Schaible, Neil Patel, Dick Tibboel, Arno van Heijst, Theodore Dassios, Joost van Rosmalen, Viviana Arreo, Yogen Singh, Karel Allegaert, Suzan Cochius-den Otter, Florian Kipfmueller, Maria Dolores Elorza, Ana Sanchez, Leopoldo Martinez, Carlos Labrandero, Pertierra Cortada, Jordi Clotet Caba, Marta Aguar, Ana Gimeno, Raquel Escrig, Irma Capolupo, Pietro Bagolan, Fabrizio Ciralli, Genny Raffaeli, Giacomo Cavallaro, Valentina Condò, Paul D. Losty, Marie Horan, Nimish V. Subhedar, Emma E. Williams, Jennifer B. Brandt, Alexandra Kreissl, Berndt Urlesberger, Carmen Mesas Burgos, Björn Frenckner, Björn Larrson, Carla Pinto, Joana Saldaha, Anne Debeer, Anne Smits, Ragnhild Emblem, Richard Keijzer, Yassar Elsayed, David Tingay, Ulrike Kraemer

“…Different therapies are still mainly based on ‘trial and error’. Inhaled nitric oxide (iNO) is often the drug of first choice. …”
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EU-GAN: A root inpainting network for improving 2D soil-cultivated root phenotyping by Shangyuan Xie, Jiawei Shi, Wen Li, Tao Luo, Weikun Li, Lingfeng Duan, Peng Song, Xiyan Yang, Baoqi Li, Wanneng Yang

“…Compared with other methods, our approach significantly improves the recall rate from 17.35 % to 35.75 % on the test dataset of 122 cotton root images, revealing improved inpainting capabilities. The trait error reduction rates (TERRs) for the root area, root length, convex hull area, and root depth are 76.07 %, 68.63 %, 48.64 %, and 88.28 %, respectively, enabling a substantial improvement in the accuracy of root phenotyping. …”
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Mixed reality infrastructure based on deep learning medical image segmentation and 3D visualization for bone tumors using DCU-Net by Kun Wang, Yong Han, Yuguang Ye, Yusi Chen, Daxin Zhu, Yifeng Huang, Ying Huang, Yijie Chen, Jianshe Shi, Bijiao Ding, Jianlong Huang

“…Methods: We conducted experiments using a hospital dataset for bone tumor segmentation, used the optimized DCU-Net and 3D reconstruction technology to generate bone tumor models, and used set similarity (DSC), recall (R), precision (P), and 3D vertex distance error (VDE) to evaluate segmentation performance and 3D reconstruction effects. …”
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A Refined Astrometric Approach Based on the Precision Premium and its Application to the Jupiter’s Satellite Himalia by Z. J. Zheng, Q. Y. Peng, F. R. Lin, D. Li

“…Since the advent of Gaia catalog, positional precision of a ground-based telescope can be greatly improved by correction of more subtle errors, including positional biases induced by atmospheric turbulence, and some instrumental factors such as geometric distortion and the charge transfer efficiency. …”
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Dynamic Surgical Prioritization: A Machine Learning and XAI-Based Strategy by Fabián Silva-Aravena, Jenny Morales, Manoj Jayabalan, Muhammad Ehsan Rana, Jimmy H. Gutiérrez-Bahamondes

“…Additionally, our methodology achieves a reduction in waiting times by up to 26%, demonstrating its effectiveness in optimizing surgical prioritization. …”
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Regulatory T Cell Responses in Participants with Type 1 Diabetes after a Single Dose of Interleukin-2: A Non-Randomised, Open Label, Adaptive Dose-Finding Trial. by John A Todd, Marina Evangelou, Antony J Cutler, Marcin L Pekalski, Neil M Walker, Helen E Stevens, Linsey Porter, Deborah J Smyth, Daniel B Rainbow, Ricardo C Ferreira, Laura Esposito, Kara M D Hunter, Kevin Loudon, Kathryn Irons, Jennie H Yang, Charles J M Bell, Helen Schuilenburg, James Heywood, Ben Challis, Sankalpa Neupane, Pamela Clarke, Gillian Coleman, Sarah Dawson, Donna Goymer, Katerina Anselmiova, Jane Kennet, Judy Brown, Sarah L Caddy, Jia Lu, Jane Greatorex, Ian Goodfellow, Chris Wallace, Tim I Tree, Mark Evans, Adrian P Mander, Simon Bond, Linda S Wicker, Frank Waldron-Lynch

“…Primary analysis of the evaluable population (n = 39) found that the optimal doses of aldesleukin to induce 10% and 20% increases in Tregs were 0.101 × 106 IU/m2 (standard error [SE] = 0.078, 95% CI = -0.052, 0.254) and 0.497 × 106 IU/m2 (SE = 0.092, 95% CI = 0.316, 0.678), respectively. …”
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Designing a Dynamic Model for Evaluating Construction Investment Projects A System Dynamics Approach by Majid Moatamedi, Mohammad Hossein Darvish Motevlli

“…The results showed that these two values behave similar to each other. The error value of the model in the predicted value and the actual value is very small on average, which indicates the high accuracy of the model in predicting the behavior of the reference. …”
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The potential of combining passenger rail with freight: A New York City case study by Pouria Bacher, Jacqueline M. Klopp, Martina Ortbauer, Maximilian Lackner

“…We visualized a freight train timetable and graph, analyzed meet-errors between freight and passenger trains, and formulated prevention policies. …”
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Rapid detection of volatile fatty acids in biogas slurry using near-infrared spectroscopy combined with optimized wavelength selection and partial least squares regression by Honglin Wang, Jinming Liu, Pengfei Li, Chong Tan

“…Model validation yielded determination coefficients of 0.9882, 0.9452, and 0.9483, and root mean square errors of prediction of 0.1193, 0.1178, and 0.5492 for acetic acid, propionic acid, and total acid, respectively, representing reductions of approximately 40 %, 18 %, and 30 % compared to full-spectrum modeling. …”
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Experimental investigation and modeling of heat and moisture transfer beneath asphalt pavements under rainfall conditions by Weihang Hua, Long Jin, Miaomiao Bai, Huimei Zhang

“…Evaporative latent heat significantly suppressed surface warming, reducing the temperature rise at 2 cm by 59.2 %, and delayed heat transfer to deeper layers (reductions of 54.5 %–64.7 %). A cumulative heat flux prediction model, incorporating solar radiation, evaporation, convection, and surface wetting, showed high accuracy (R2 = 0.981 and 0.952; relative errors: 4.1 % and 9.6 %). …”
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ResT-IMU: A Two-Stage ResNet-Transformer Framework for Inertial Measurement Unit Localization by Yanping Zhu, Jianqiang Zhang, Wenlong Chen, Chenyang Zhu, Sen Yan, Qi Chen

“…Theexperimental results demonstrate that ResT-IMU achieves velocity prediction errors of 0.0182 m/s on the iIMU-TD dataset and 0.014 m/s on the RoNIN dataset. …”
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Challenges in Collating Spirometry Reference Data for South-Asian Children: An Observational Study. by Sooky Lum, Vassiliki Bountziouka, Philip Quanjer, Samatha Sonnappa, Angela Wade, Caroline Beardsmore, Sunil K Chhabra, Rajesh K Chudasama, Derek G Cook, Seeromanie Harding, Claudia E Kuehni, K V V Prasad, Peter H Whincup, Simon Lee, Janet Stocks

“…Records with transcription errors, missing values for height or spirometry, and implausible values were excluded(n = 110).…”
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Adaptive Echo State Network for crop yield prediction incorporating Fall Armyworm dynamicsMendeley Data by Mulima Chibuye, Jackson Phiri, Phillip Nkunika

“…In cross-validation, the FAW-aware ESN achieves an R² of ∼0.55 and reduces prediction errors by up to 67 % versus unpenalized baselines, closely capturing observed yield reductions exceeding 20 % during severe outbreaks. …”
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Lightweight SCD-YOLOv5s: The Detection of Small Defects on Passion Fruit with Improved YOLOv5s by Yu Zhou, Zhenye Li, Sheng Xue, Min Wu, Tingting Zhu, Chao Ni

“…Compared with manual detection, the proposed model enhances detection efficiency by reducing errors caused by subjective judgment. It also achieves faster inference speed (26.66 FPS), and reductions of 9.6% in parameters and 8.6% in weight size, while maintaining high detection performance. …”
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