Analytical simulation of the nonlinear Caputo fractional equations by Ali Ahadi, Seyed Mostafa Mousavi, Amir Mohammad Alinia, Hossein Khademi

“…The accuracy and effectiveness of the methods are demonstrated through detailed comparisons with exact solutions and previous works in the field.The study highlights the strengths of each technique in handling non-linear and fractional-order problems, providing reliable results with minimal error. Specifically, the HPM and VIM show remarkable convergence properties, while the AGM proves efficient in solving both linear and non-linear equations. …”
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Photonics-Assisted Non-Scanning High-Accuracy Frequency Measurement Using Low-Speed Components by Zhiqiang Shen, Chenfei Jin, Qunsong He, Zijing Zhang, Yuan Zhao

“…Instantaneous frequency measurement of microwave signals is a fundamental functionality for applications including radar and electronic warfare. …”
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In-situ direct shear test and numerical comparative research on mudstone gravel slope reinforced by polymer grouting by Mengqiang Shen, Zhichao Zhang, Changguang Qi, Yihan Lu, Rongyue Zheng

“…The discrepancy between simulation and experimental data is minimal, with the simulated internal friction angle deviating by -0.97–1.5% from the experimental values and the simulated cohesion being marginally lower, with an error range of 3.9-5%. These results offer valuable insights for engineering applications.…”
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Asymptotic Synchronization for Caputo Fractional-Order Time-Delayed Cellar Neural Networks with Multiple Fuzzy Operators and Partial Uncertainties via Mixed Impulsive Feedback Cont... by Hongguang Fan, Chengbo Yi, Kaibo Shi, Xijie Chen

“…In response to the properties of the considered cellar neural networks (NNs), this article designs a new class of mixed control protocols that utilize both the error feedback information of systems and the sampling information of impulse moments to achieve network synchronization tasks. …”
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A Ring2Vec description method enables accurate predictions of molecular properties in organic solar cells by Ting Zhang, Kangzhong Wang, Kunlei Jing, Gang Li, Qing Li, Chen Zhang, He Yan

“…We achieve fast and accurate predictions of the energy levels of NFA molecules, with a minimal prediction error of merely 0.06 eV. Furthermore, our method can potentially have broad applicability across various domains of molecular description and property prediction, owing to the efficiency of the Ring2Vec model.…”
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Rapid Assessment of the Fatigue Limit Using an Iterative Algorithm Applied to Intrinsic Dissipation by Luca Santoro, Raffaella Sesana, Francesca Maria Curà

“…The algorithm seeks to minimize the error between experimental data and the fitted curves, ensuring continuity at the estimated fatigue limit. …”
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A long-term high-resolution air quality reanalysis with a public-facing air quality dashboard over the Contiguous United States (CONUS) by R. Kumar, P. Bhardwaj, P. Bhardwaj, C. He, J. Boehnert, F. Lacey, S. Alessandrini, K. Sampson, M. Casali, S. Swerdlin, O. Wilhelmi, G. G. Pfister, B. Gaubert, H. Worden

“…The mean bias and root-mean-square error for modeled ozone are 3.7–6.8 and 7–9 ppbv, respectively, while the corresponding values for PM<span class="inline-formula">_2.5</span> are <span class="inline-formula">−</span>0.9–5.6 and 3.0–8.3 <span class="inline-formula">µg</span> m<span class="inline-formula">⁻³</span>, respectively. …”
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大型可展开网状天线反枕效应研究进展 by 贺乃馨, 宋燕平, 李团结, 张大羽, 黄鹏飞, 李怡晨, 曾家琛

“…Among them, the anti-pillow effect is one of the main sources of the design error of mesh antenna. It will make the antenna deviate from the ideal paraboloid, affect the far-field characteristics of the antenna, and then affect the communication function of the satellite.ProgressThe anti-pillow effect can be expressed by the negative Gauss curvature surface in mathematics. …”
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Soil moisture product consistency for operational drought monitoring in Europe by J. Gaona, D. Bavera, G. Fioravanti, S. Hahn, P. Stradiotti, P. Filippucci, S. Camici, L. Ciabatta, H. Mosaffa, S. Puca, N. Roberto, L. Brocca

“…Then triplets of the active, passive and model-based products are applied triple collocation analysis (TCA) to assess their performance based on TCA metrics such as the correlation, error variance, sensitivity and signal-to-noise ratio.…”
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Interpretable Machine Learning for Legume Yield Prediction Using Satellite Remote Sensing Data by Theodoros Petropoulos, Lefteris Benos, Remigio Berruto, Gabriele Miserendino, Vasso Marinoudi, Patrizia Busato, Chrysostomos Zisis, Dionysis Bochtis

“…Machine Learning (ML) has shown promise in this field; however, its application to legume crops, especially to lupin, remains limited, while many models lack interpretability, hindering real-world adoption. …”
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Entropy generation assessment in radiative rheological nanomaterial beyond conventional approach of heat and mass fluxes by T. Hayat, Aqsa Razzaq, Sajjad Shaukat Jamal, Aneeta Razaq, Sohail A. Khan

“…Solutions convergence through individual and total residual errors is organized. Results: Flow, temperature, entropy rate, and concentration are deliberated. …”
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A Review of Advanced Deep Learning Methods of Multi-Target Segmentation for Breast Cancer WSIs by Qiaoyi Xu, Afzan Adam, Azizi Abdullah, Nurkhairul Bariyah

“…However, manually reviewing whole slide images (WSIs) for tissue segmentation is time-consuming and prone to errors, highlighting the need for multi-target deep learning models to automate the segmentation of these complex structures. …”
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Temperature-Insensitive Second-Order Microring Resonator for Dense Wavelength Division Multiplexing (DWDM) by Fuling Wang, Xiao Xu, Chonglei Sun, Liuge Du, Jia Zhao

“…Due to the fabrication imperfections and process errors, the central wavelengths shifted from ideal values. …”
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Revolutionizing Chinese medicine granule placebo with a machine learning four-color model by Tingting Teng, Jingze Zhang, Peiqi Miao, Lipeng Liang, Xinbo Song, Dailin Liu, Junhua Zhang

“…However, due to the diverse colors and complex color gamut of these particles, existing simulation methods rely on manual comparison and color mixing, leading to high subjectivity and errors. This study addresses this issue by developing a prediction model to accurately simulate the colors of Chinese medicine granules. …”
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Large-area urban TomoSAR method with limited a priori knowledge and a complex deep learning model by Haoxuan Duan, Yuzhou Liu, Hong Zhang, Peifeng Ma, Zhongqi Shi, Zihuan Guo, Yixian Tang, Fan Wu, Chao Wang

“…Validation via corner reflectors deformation monitoring confirmed reliability, with a 1.5 mm average error. These results highlight the practical applicability of the proposed method for large-scale urban monitoring and its potential to provide technical support for sustainable urban development.…”
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Exploring Machine Learning Models for Vault Safety in ICL Implantation: A Comparative Analysis of Regression and Classification Models by Qing Zhang, Qi Li, Zhilong Yu, Ruibo Yang, Emmanuel Eric Pazo, Yue Huang, Hui Liu, Chen Zhang, Salissou Moutari, Shaozhen Zhao

“…Model performance was evaluated using metrics including the mean absolute error (MAE) and root mean squared error (RMSE) for regression models, while accuracy, F1-score, and area under the curve (AUC) were used for classification models. …”
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Improving Accuracy and Calibration of Deep Image Classifiers With Agreement-Driven Dynamic Ensemble by Pedro Conde, Rui L. Lopes, Cristiano Premebida

“…Possible strategies to tackle this problem are two-fold: (i) models need to be highly accurate, consequently reducing this risk of failure; (ii) facing the impossibility of completely eliminating the risk of error, the models should be able to inform the level of uncertainty at the prediction level. …”
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Mapping of processes and risks in the digital transformation in metrology of ionizing radiation, a case study in X-rays air kerma calibration by Igor Fernando Modesto Garcia, Jeovana Santos Ferreira, Eric Matos Macedo, Marcos Vinicius Teixeira Navarro, José Guilherme Pereira Peixoto

“…It is also possible to estimate that artificial intelligence and automation can significantly reduce measurement risks, identification, and error in the analysis and use of calibration certificates.…”
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A fast physics-based perturbation generator of machine learning weather model for efficient ensemble forecasts of tropical cyclone track by Jingchen Pu, Mu Mu, Jie Feng, Xiaohui Zhong, Hao Li

“…Although emerging artificial intelligence (AI)-based weather models offer high forecast accuracy and improved computational efficiency, they still face considerable challenges in ensemble forecasting applications, due to the unclear error growth dynamic and the lack of suitable ensemble methods in AI-based models. …”
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Fusing LiDAR and Photogrammetry for Accurate 3D Data: A Hybrid Approach by Rytis Maskeliūnas, Sarmad Maqsood, Mantas Vaškevičius, Julius Gelšvartas

“…Experimental results, using a custom dataset of real-world scenes, demonstrate that the hybrid fusion method achieves an average error of less than 5% in the measurements of small reconstructed objects, with large objects showing less than 2% deviation from real sizes. …”
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