Measuring technical variability in illumina DNA methylation microarrays. by Anderson A Butler, Jason J Kras, Karolina P Chwalek, Enrique I Ramos, Isaac J Bishof, David S Vogel, Daniel L Vera

“…Additionally, our investigation identified outliers in low-level fluorescence data which might play a role in contributing to predictive error in computational models of health-relevant traits such as age.…”
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Heartbeat Biometrics for Remote Authentication Using Sensor Embedded Computing Devices by Md Saiful Islam

“…The biometric verification method was tested with ECG records of 100 individuals and 1.34% of equal error rate (EER) was obtained. This feature could be computed and transmitted efficiently using mobile computing devices. …”
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Comparison of artificial neural network and response surface methodology prediction in key performance of two-component grout material in shield tunneling by Kailong Lu, Xudong Chen, Jiahong Zhang, Jiaming Chen, Zhenwei Liu, Lulu Chen

“…Specifically, ANN achieved a higher coefficient of determination (R2) and lower prediction errors for all target indicators. These findings confirm the superior modeling capability of ANN and provide practical guidance for the performance-driven mix design of two-component grout materials in tunneling applications.…”
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Coarse-to-Fine Approach: Automatic Delineation of Kidney Ultrasound Data by Tao Peng, Yiwen Ruan, Yidong Gu, Jiang Huang, Caiyin Tang, Jing Cai

“…Third, by utilizing the characteristics of the principal curve, a neural network automatically refines curve shapes, thus reducing model errors. Finally, we employ an intelligent searching polyline segment method for automatic kidney contour segmentation. …”
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Invited perspectives: safeguarding the usability and credibility of flood hazard and risk assessments by B. Merz, B. Merz, G. Blöschl, R. Jüpner, H. Kreibich, K. Schröter, S. Vorogushyn

“…Our framework adds crucial dimensions to current validation approaches, such as the need to understand the possible impacts on society when the assessment has large errors. It further emphasizes the essential role of stakeholder participation, objectivity, and verifiability in assessing flood hazard and risk. …”
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Dynamic Localized SNV, Peak SNV, and Partial Peak SNV: Novel Standardization Methods for Preprocessing of Spectroscopic Data Used in Predictive Modeling by Emily Grisanti, Maria Totska, Stefan Huber, Christina Krick Calderon, Monika Hohmann, Dominic Lingenfelser, Matthias Otto

“…All proposed methods are able to significantly improve the model performance in cross validation and robustness tests compared to SNV. The prediction errors could be reduced by up to 16% and 29% compared with LSNV for two regression models.…”
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Investigating Visual Localization Using Geospatial Meshes by F. Vultaggio, F. Vultaggio, P. Fanta-Jende, M. Schörghuber, A. Kern, M. Gerke

“…Visual localization, essential for applications such as robotics and augmented reality, traditionally relies on Structure-from-Motion (SfM) reconstructions or image collections as maps. …”
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Adaptable gene‐specific dye bias correction for two‐channel DNA microarrays by Thanasis Margaritis, Philip Lijnzaad, Dik van Leenen, Diane Bouwmeester, Patrick Kemmeren, Sander R van Hooff, Frank CP Holstege

“…This results in large measurement errors, which vary considerably for different probes and also across different hybridizations. …”
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Subsurface Geological Profile Interpolation Using a Fractional Kriging Method Enhanced by Random Forest Regression by Qile Ding, Yiren Wang, Yu Zheng, Fengyang Wang, Shudong Zhou, Donghui Pan, Yuchun Xiong, Yi Zhang

“…The results indicate that the proposed model reduces prediction errors and enhances spatial prediction reliability compared to other models. …”
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Efficiently charting the space of mixed vacancy-ordered perovskites by machine-learning encoded atomic-site information by Fan Zhang, Li Fu, Weiwei Gao, Peihong Zhang, Jijun Zhao

“…This approach accurately predicts band gaps and formation energies for mixed VODPs, achieving Root Mean Square Errors of 21 meV and 3.9 meV/atom, respectively. Trained with samples with up-to three mixed elements and small supercells (<72 atoms), the model not only can be generalized to medium- and high-entropy systems and larger supercells (>200 atoms), but also well reproduces the bandgap bowing effect in Sn-based mixed VODPs.…”
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Predicting biogas production in real scale anaerobic digester under dynamic conditions with machine learning approach by M. Erdem Isenkul, Sevgi Güneş-Durak, Yasemin Poyraz Kocak, İnci Pir, Mertol Tüfekci, Güler Türkoğlu Demirkol, Selçuk Sevgen, Aslı Seyhan Çığgın, Neşe Tüfekci

“…Hyperparameter tuning was conducted to enhance prediction accuracy while reducing model error. The findings demonstrated that ML-based modelling can serve as a reliable and effective tool to improve biogas production efficiency in wastewater treatment applications. …”
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A Feasibility Study on Indoor Localization and Multiperson Tracking Using Sparsely Distributed Camera Network With Edge Computing by Hyeokhyen Kwon, Chaitra Hegde, Yashar Kiarashi, Venkata Siva Krishna Madala, Ratan Singh, ArjunSinh Nakum, Robert Tweedy, Leandro Miletto Tonetto, Craig M. Zimring, Matthew Doiron, Amy D. Rodriguez, Allan I. Levey, Gari D. Clifford

“…Our pipeline demonstrated an average localization error of 1.41 m, a multiple-object tracking accuracy score of 88.6&#x0025;, and a mean absolute body orientation error of 29<inline-formula><tex-math notation="LaTeX">$^{\circ }$</tex-math></inline-formula>. …”
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From lab to field with machine learning – Bridging the gap for movement analysis in real-world environments: A commentary by Carlo Dindorf, Fabian Horst, Djordje Slijepčević, Bernhard Dumphart, Jonas Dully, Matthias Zeppelzauer, Brian Horsak, Michael Fröhlich

“…Despite the advancements of ML in biomechanics, central challenges are faced. Estimation errors remain critically high depending on the task and application field, necessitating a careful reflection on data acquisition potentials. …”
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Quasiperiodic Dielectric Gratings for Multiband Fiber-To-Chip Couplers by Odair J. Picin, Faustino Reyes Gomez, Ernesto Reyes-Gomez, Osvaldo N. Oliveira, J. Ricardo Mejia-Salazar

“…Moreover, the allowed modes in the grating are almost insensitive to fiber misalignments and small fabrication errors for high Fibonacci steps, which is useful when alignment of optical components is impractical. …”
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Assessment of Global and Detailed Chemical Kinetics in Supercritical Combustion for Hydrogen Gas Turbines by Sylwia Oleś, Jakub Mularski, Halina Pawlak-Kruczek, Abhishek K. Singh, Artur Pozarlik

“…The results indicate good agreement between the global and detailed mechanisms, with average relative errors below 2% for supercritical argon and 4% for supercritical carbon dioxide. …”
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Integrating Sentinel-1 SAR and Machine Learning Models for Optimal Soil Moisture Sensor Placement at Catchment Scale by Yi Xie, Guotao Cui, Kaifeng Zheng, Guoping Tang

“…Results show that a network of nine optimally placed sensors minimized prediction errors (RMSE: 7.20%), outperforming both sparser and denser configurations. …”
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Effectiveness of Sound Field Corrections for High-Frequency Pressure Comparison Calibration of MEMS Microphones by Fabio Saba, María Campo-Valera, Davide Paesante, Giovanni Durando, Mario Corallo, Diego Pugliese

“…Furthermore, the implementation of a measurement set-up, which includes the insert voltage technique, allows for an accurate assessment of the preamplifier gain and minimizes systematic errors. Experimental validation shows that the refined calibration methodology produces highly reliable correction values, ensuring a robust performance over a wide frequency range (20 Hz–20 kHz). …”
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Topology-Aware Anchor Node Selection Optimization for Enhanced DV-Hop Localization in IoT by Haixu Niu, Yonghai Li, Shuaixin Hou, Tianfei Chen, Lijun Sun, Mingyang Gu, Muhammad Irsyad Abdullah

“…This strategy eliminates anchor nodes with high estimation errors and selects a subset of high-quality anchors to improve the localization of unknown nodes. …”
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Enhancing Multi-Flight Unmanned-Aerial-Vehicle-Based Detection of Wheat Canopy Chlorophyll Content Using Relative Radiometric Correction by Jiale Jiang, Qianyi Zhang, Shuai Gao

“…The predictive accuracy of CCC models improved after the relative radiometric correction, with validation errors decreasing by 17.1–45.6% across different growth stages and with full-season integration yielding a 44.3% reduction. …”
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Model-based iterative reconstruction with adaptive regularization for artifact reduction in electron tomography by Singanallur Venkatakrishnan, Obaidullah Rahman, Lynda Amichi, Jose D. Arregui-Mena, Haoran Yu, David A. Cullen, Amirkoushyar Ziabari

“…These artifacts lead to errors in the downstream characterization affecting extraction of critical features such as the size of the metal particles, their distribution and the volume of the lighter support regions. …”
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