Numerical Analysis for a Class of Variational Integrators by Yihan Shen, Yajuan Sun

“…The framework provides tools applicable to geometric numerical integration of both ordinary and partial differential equations.…”
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A Novel Sub-Type Mean Estimator for Ranked Set Sampling with Dual Auxiliary Variables by Eda Gizem Koçyiğit

“…The mathematical formulas of the proposed estimator’s mean square error and bias are presented and theoretically contrasted with an analogous estimator found in the existing best sub-estimator literature. …”
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Data-driven thrust prediction in applied-field magnetoplasmadynamic thrusters for space missions using artificial intelligence-based models by Tarik Pinaffo Almeida, Shahin Alipour Bonab, Mohammad Yazdani-Asrami

“…With a Goodness of Fit ( R ^2 ) of 98.55%, root mean square error of 1.421 N, and mean absolute error of 0.453 N, XGBoost specifically, and AI in general, has demonstrated its superiority, by significantly improving on the accuracy of previously published empirical models for AF-MPDT thrust prediction. …”
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Time-Series Prediction of Failures in an Industrial Assembly Line Using Artificial Learning by Mert Can Sen, Mahmut Alkan

“…The study underscores NAR as optimal for precision-critical aerospace applications, where error minimisation and generalisability are paramount. …”
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On precise path planning algorithm in wireless sensor network by Farhanda Javed, Samiullah Khan, Asfandyar Khan, Alweena Javed, Rohi Tariq, Matiullah, Faheem Khan

“…Location information of sensor node is a critical issue for many applications in wireless sensor network. The main problem is to design a path for a mobile landmark to maximize the location accuracy as well as to reduce energy consumption. …”
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Design and robust control of a dual-loop valve-controlled cylinder system by Lijuan Zhao, Tiangu Wu

“…Experimental and simulation results demonstrate that, compared to a conventional Sliding Mode Controller (SMC), the proposed ESMO-RSMC strategy reduces the peak disturbance error by 60.4% and shortens the initial convergence time from 0.08 s to an immediate response within the error band. …”
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An Improvement of the Load Transfer Method for Energy Piles Under Thermo-Mechanical Loads by Haofan Yang, Haowen Pan, Chenfeng Zong, Ziyi Wang, Gang Jiang

“…The calculated results at the pile head show an 18% reduction in error compared to previous studies. The average error compared with field test results is within 20%, with consistent trend patterns, confirming the feasibility of the proposed method. …”
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Multi-group global tests for restricted mean survival time and restricted mean time lost: a variable transformation approach by Shuyu Chen, Mengyao Wang, Xingyou Zhou, Wenbin Zhang, Chengfeng Zhang, Zheng Chen

“…However, asymptotic approaches for these metrics suffer from inflated type I error in small samples, limiting their reliability. …”
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Mapping total suspended solids (TSS) and dissolved organic carbon (DOC) in complex coastal waters using deep learning enhanced remote sensing by S.L. Kesav Unnithan, Nagur Cherukuru, Timothy Ingleton, Eric Lehmann, Matt Paget, Yiqing Guo, Nathan Drayson, Gemma Kerrisk

“…We incorporate uncertainty into the WQ−IOP measurements and simulate an augmented spectral library for training a DL model for aquatic Remote Sensing (DL-RS) applications. The model error analysis revealed a) an inherent Mean Absolute Error (MAE) of 7 % for TSS and 22 % for DOC, b) a predictive MAE of 75 % (TSS) and 31 % (DOC) in temperate waters, and c) an in-situ optical closure predictive MAE of 18 % and a Mean Percentage Error of ±5 % upon comparing forward modelled Rrs outputs from DL-RS-derived IOP and WQ parameters with atmospherically corrected Landsat-8 imagery (2013−2022). …”
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Real-time and resource-efficient banana bunch detection and localization with YOLO-BRFB on edge devices by Shuo Wang, Shuo Wang, Shuo Wang, Lijiao Wei, Danran Zhang, Danran Zhang, Ling Chen, Ling Chen, Weihua Huang, Dongjie Du, Kangmin Lin, Zhenhui Zheng, Zhenhui Zheng, Zhenhui Zheng, Jieli Duan

“…The average positioning error of the system along the X-axis is 12.33 mm, the average positioning error along the Y-axis is 11.11 mm, and the average positioning error along the Z-axis is 16.33 mm. …”
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A New Frontier in Wind Shear Intensity Forecasting: Stacked Temporal Convolutional Networks and Tree-Based Models Framework by Afaq Khattak, Jianping Zhang, Pak-wai Chan, Feng Chen, Abdulrazak H. Almaliki

“…The proposed TCN-XGBoost model outperformed these alternatives, achieving a lower Root Mean Squared Error (RMSE: 1.95 for training, 1.97 for testing), Mean Absolute Error (MAE: 1.41 for training, 1.39 for testing), and Mean Absolute Percentage Error (MAPE: 7.90% for training, 7.89% for testing). …”
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Inverse distance weight interpolation method for missing data of PM2.5 spatiotemporal series by Yurou LIANG, Hongling WU, Weipeng WANG, Feng CHENG, Ping DUAN

“…Interpolation accuracy was evaluated using four metrics: root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and mean relative error (MRE). …”
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Assessment of tensile and fatigue behavior of PEEK in a physiologically relevant environment by Mirco Peron, Jan Torgersen, Filippo Berto

“…Among them, polyetheretherketone (PEEK) has gained wide interest in load-bearing applications such as spinal cages due to its yielding behavior and its superior corrosion resistance. …”
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The dual-edged potential of AI autonomously defining loss functions by Abbas Ghori

“…This paper discusses classical loss functions, including mean squared error (MSE), cross-entropy, Huber loss, and a range of other problem-specific variants that appear in scenarios dealing with imbalanced data, adversarial learning, and reinforcement learning. …”
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Topological Sequences Connected With Inverse Graphs of Finite Flexible Weak Inverse Property Quasigroups: An Approach From Polynomials to Machine Learning by Faizah D. Alanazi

“…They indicate feature significance, nonlinear interactions, and error dynamics. Moreover, they empower models to extrapolate complex real-world data, facilitating tasks like regression, classification, optimized performance, and feature adaptation. …”
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Impedance Characteristic-Based Frequency-Domain Parameter Identification Method for Photovoltaic Controllers by Yujia Tang, Xin Zhou, Yihua Zhu, Junzhen Peng, Chao Luo, Li Zhang, Jinling Qi

“…The maximum identification error of other intelligent algorithms was 5%, with a difference of less than 1% compared to the proposed method. …”
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A fast monocular 6D pose estimation method for textureless objects based on perceptual hashing and template matching by Jose Moises Araya-Martinez, Jose Moises Araya-Martinez, Vinicius Soares Matthiesen, Vinicius Soares Matthiesen, Simon Bøgh, Jens Lambrecht, Rui Pimentel de Figueiredo

“…Object pose estimation is essential for computer vision applications such as quality inspection, robotic bin picking, and warehouse logistics. …”
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On the Evaluation of the Hyperthermic Efficiency of Magnetic Scaffolds by Matteo B. Lodi, Antonios Makridis, Konstantina Kazeli, Theodoros Samaras, Makis Angelakeris, Giuseppe Mazzarella, Alessandro Fanti

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Out-of-vehicle time-of-arrival-based localization in ultra-wide band by Pavel Kukolev, Aniruddha Chandra, Tomáš Mikulášek, Aleš Prokeš

“…The article presents out-of-vehicle channel measurement results in ultra-wide band spanning 3–11 GHz bandwidth using a vector network analyzer for localization applications. Experiments for different distances and different angles around the parked car are carried out. …”
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Interrelationships Among Government Participation, Population and Growth of per Capita Income: Inquiry on Top Twenty Income-Holding Countries in the World by Ramesh Chandra Das

“…The literature on growth in economics encompasses two main facets of thinking: the applicability of diminishing productivity of capital, as has been in the neoclassical growth model with exogenous technological progress, and the applicability of non-diminishing productivity of capital, as has been in the endogenous growth models. …”
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