3D convolutional deep learning for nonlinear estimation of body composition from whole body morphology by Isaac Y. Tian, Jason Liu, Michael C. Wong, Nisa N. Kelly, Yong E. Liu, Andrea K. Garber, Steven B. Heymsfield, Brian Curless, John A. Shepherd

“…Deep shape features produced 6–8% reduction in prediction error over linear PCA features for males only, and a 4–14% reduction in precision error for both sexes. …”
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Funding the future: Nigeria's battle against poverty through government expenditure by Temitope Adebayo

“…However, the error correction mechanism indicates a notably slow adjustment process (-0.000376), suggesting structural impediments to poverty reduction efforts. …”
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Optimizing Supply Chain Resilience Using Advanced Analytics and Computational Intelligence Techniques by Jie Xu, Lixing Bo

“…It achieves a 12% reduction in overall SCM costs, improves demand forecasting accuracy with reduced mean absolute error (MAE) and root mean squared error (RMSE), and enhances resource utilization efficiency by up to 20%. …”
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The role of precision tool positioning in enhancing machining accuracy by Tuyboyov Oybek, Baydullayev Azamat, Jeltuxin Andrey, Muxiddinov Zayniddin

“…Utilizing tools such as laser interferometers, autocollimators, and predictive thermal simulations, the research addresses critical challenges in tool positioning accuracy caused by thermal fluctuations and geometric errors. Compensation strategies reduced thermal errors by 15%, while kinematic calibration mitigated angular discrepancies by 10%. …”
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Multimodal Collaborative Perception for Dynamic Channel Prediction in 6G V2X Networks by Ghazi Gharsallah, Georges Kaddoum

“…Compared to baseline methods—namely, a classical LS-LMMSE approach and a wireless-based model that solely relies on channel measurements—our framework achieves up to a 30.82% reduction in mean squared error (MSE) and a 32.76% increase in goodput. …”
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Tibiofemoral Joint Contact Force Estimation Based on OpenSim Musculoskeletal Modeling by WANG Xiaoling, JIAN Jiawei, XIE Qiurong, LIAN Zhanghui, GUO Chunming, GUO Jiemei, LI Yurong

“…The root mean square error of the estimated and measured peak contact force of the medial and lateral sides were 0.43±0.25 BW and 0.34±0.24 BW, and the errors at the time of peak contact force onset were 44.09±34.66 ms and 67.52±61.19 ms. …”
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Fundamentals of Industrial Strategizing: Formation of the Concept by N. I. Sasaev

“…Since the most critical errors occur at the initial stages of strategizing, to a greater extent this research is focused on the formation of the concept — the core block of any strategy. …”
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Practical techniques for high-precision measurements on near-term quantum hardware and applications in molecular energy estimation by Keijo Korhonen, Hetta Vappula, Adam Glos, Marco Cattaneo, Zoltán Zimborás, Elsi-Mari Borrelli, Matteo A. C. Rossi, Guillermo García-Pérez, Daniel Cavalcanti

“…We demonstrate these techniques via molecular energy estimation of the BODIPY molecule on a Hartree-Fock state on an IBM Eagle r3, obtaining a reduction in measurement errors by an order of magnitude from 1-5% to 0.16%. …”
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Extended Calibration Technique of a Four-Hole Probe for Three-Dimensional Flow Measurements by Suresh Munivenkatareddy, Nekkanti Sitaram

“…Sensitivity analysis of all the four calibration coefficients shows that probe pitch sensitivity is lower than the yaw sensitivity in the center zone, and extended left and right zones have lower sensitivity than the center zone. In addition, errors due to the data reduction program for the probe are presented. …”
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Effective least squares approximation method for estimating the rhythm function of cyclic random process by Serhii Lupenko, Małgorzata Wiatr, Andrzej Metelski

“…Analytical dependencies between errors of estimation of a discrete rhythm function and errors of segmentation of a cyclic random process into cycles and zones were constructed. …”
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Physics-informed transformation toward improving the machine-learned NLTE models of ICF simulations by Min Sang Cho, Paul E. Grabowski, Kowshik Thopalli, Thathachar S. Jayram, Michael J. Barrow, Jayaraman J. Thiagarajan, Rushil Anirudh, Hai P. Le, Howard A. Scott, Joshua B. Kallman, Branson C. Stephens, Mark E. Foord, Jim A. Gaffney, Peer-Timo Bremer

“…By replacing the costly nonlocal thermodynamic equilibrium (NLTE) model with machine-learning models, significant reductions in calculation time have been achieved. However, determining how to optimize machine-learning-based NLTE models in order to match ICF simulation dynamics remains challenging, underscoring the need for physically relevant error metrics and strategies to enhance model accuracy with respect to these metrics. …”
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Energy Consumption Prediction for Drilling Pumps Based on a Long Short-Term Memory Attention Method by Chengcheng Wang, Zhi Yan, Qifeng Li, Zhaopeng Zhu, Chengkai Zhang

“…In the context of carbon neutrality and emission reduction goals, energy consumption optimization in the oil and gas industry is crucial for reducing carbon emissions and improving energy efficiency. …”
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Assessing the Reliability of Predicted Decadal Surface Temperatures in Southeast Asia by Dara Kasihairani, Rahmat Hidayat, Supari Supari

“…The metrics of Anomaly Correlation Coefficient (ACC) and Mean Error (ME) are employed to assess the model outputs, with 51 hindcast datasets spanning initial years from 1960 to 2010 and ERA5 reanalysis data serving as the reference. …”
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Ensemble learning methods with single and multi-model deep learning approaches for cephalometric landmark annotation by S. Rashmi, S. Srinath, R. Rakshitha, B. V. Poornima

“…The ensemble meta-model further boosts accuracy to 83.61% and 95.4%, respectively, reducing mean radial errors by 0.38 mm and 0.33 mm. These results highlight significant improvements in accuracy and error reduction through strategic combinations of deep learning architectures and ensemble techniques demonstrating the ability to significantly enhance cephalometric landmark annotation accuracy, which is critical for the practical applicability of the methodology.…”
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Advanced sentiment analysis in online shopping: Implementing LSTM models analyzing E-commerce user sentiments by Lu Liyuan

“…Sarcasm and irony accounted for 22% of the classification errors, while mixed sentiment accounted for 18%, and implicit accounted for 15%. …”
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Application of Parametric Modeling Techniques for Automated Detailing of Exterior Finishes in Architectural BIM Models: A South Korean Case Study by Sihyun Kim, Min Htet Myint, Youngsu Yu, Wonbok Lee, Tae Wan Kim, Bonsang Koo

“…In particular, downspouts achieved a perfect accuracy score, while external wall finishes achieved a 94.42% reduction in modeling time. Modeling errors, which occurred due to inconsistencies in the test models, also demonstrate the need for specifying uniform modeling conventions of BIM model submissions in the detailed design phases.…”
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Assimilation of the total electron content obtained from GNSS to a model of the ionosphere using a hierarchical Bayesian network by Tang Jun, Hu Jiacheng, Zhang Wei, Fan Cihang, Zhou Qiangbo

“…Ionospheric data assimilation aims to address the uneven spatiotemporal distribution of observational data and errors in numerical models. This paper proposes an ionospheric data assimilation model using the hierarchical Bayesian network (HBN) algorithm. …”
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Hyperelastic and viscoelastic properties of recycled rubber material – experimental and fem characterization study by Juan S. Rodriguez-Piedrahita, Liseth A. Muñoz-Gutierrez, Ingrid E. Madera-Sierra, Diego D. Pérez-Ruiz, Orlando Cundumí-Sanchez

“…The methodology involved characterizing an RR mix through multiple deformation modes (uniaxial compression and tension, simple shear, creep, and relaxation). Using an iterative error reduction method that considered all modes simultaneously, the Bergström-Boyce model achieved an average R2 value of 0.92 for the RR matrix alone. …”
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A Short-Term Load Forecasting Method Considering Multiple Factors Based on VAR and CEEMDAN-CNN-BILSTM by Bao Wang, Li Wang, Yanru Ma, Dengshan Hou, Wenwu Sun, Shenghu Li

“…The actual simulation results utilizing the Australian data validate the forecasting accuracy of the proposed model, achieving reduction in the root mean square error by 31.21% and 18.04% compared to the VAR and CEEMDAN-CNN-BILSTM, respectively.…”
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Increasing 3D Printing Accuracy Through Convolutional Neural Network-Based Compensation for Geometric Deviations by Moustapha Jadayel, Farbod Khameneifar

“…The predicted deviation field is then inverted and applied to the digital model to generate a compensated geometry, which, when printed, offsets the errors observed in the original part. Experimental validation using a complex reference geometry shows that the proposed compensation method achieves an 88.5% reduction in mean absolute geometric deviation compared to the uncompensated print. …”
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