Common inaccuracies and errors in the application of statistical methods in soil science by V. P. Samsonova, J. L. Meshalkina

“…The most common inaccuracies and errors in the application of statistical methods found in Russian publications on soil science are considered. …”
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Development of a Human Performance Baseline of Lay Error in Targeting by Thirimachos Bourlai, Parker Ensing, Alexia Toma, Victor Philippe, Jennifer Forsythe, Cody L. Lundberg, Nicholas R. Gans

“…After data collection, statistical analyses of lay error were conducted including fitting non-Gaussian distribution functions and applying Fitts’ Law to the targeting analysis. …”
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A Study on the Filtering Method of Natural Gamma Energy Spectrum Logging Data for Low-Count Features by ZHU Qian, YU Huawei, GE Yunlong, YUE Aizhong, TONG Maosong, CHEN Jinhong, YANG Shu

“…The low count characteristic magnifies the influence of random error of formation decay, resulting in poor quality of energy spectrum measurement and large statistical noise of logging curve. …”
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Robust and Reversible Audio Watermarking by Modifying Statistical Features in Time Domain by Shijun Xiang, Le Yang, Yi Wang

“…In each frame, the use of three samples as a group generates a prediction error and a statistical feature value is calculated as the sum of all the prediction errors in the frame. …”
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THz-Enabled UAV Communications Under Pointing Errors: Tractable Statistical Channel Modeling and Security Analysis by Mohammad Javad Saber, Mazen Hasna, Osamah S. Badarneh

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Statistical Approach to Research on the Relationship Between Kp/Dst Geomagnetic Indices and Total GPS Position Error by Mario Bakota, Igor Jelaska, Serdjo Kos, David Brčić

“…Statistical evaluation was performed using One-Way Repeated Measures ANOVA to determine whether positional error variances across geomagnetic activity phases were significant. …”
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Revolutionizing classification: A novel gray level co-occurrence matrix and statistical feature-based segmentation approach by Abdelwahed Motwakel

“…Our results demonstrate significant improvements in classification accuracy, sensitivity, specificity, and error rates across various metrics and feature sets. …”
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A unique statistical framework to predict the health of a machine by utilizing the vibration features of rolling element bearing data by Saima Bhatti, Fozia Shaikh, Asif Mansoor, Asif Ali Shaikh

“…The Root Mean Square (RMS) value is a widely used statistical feature in Condition Monitoring (CM), providing a reliable and quantitative technique for detecting early-stage bearing faults. …”
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Critical evaluation of feature importance assessment in FFNN-based models for predicting Kamlet-Taft parameters by Yoshiyasu Takefuji

“…Mohan et al. developed a feed-forward neural network (FFNN) model to predict Kamlet-Taft parameters using quantum chemically derived features, achieving notable predictive accuracy. However, this study raises concerns about conflating prediction accuracy with feature importance accuracy, as high R2 and low root mean square error (RMSE) do not guarantee valid feature importance assessments. …”
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Development of New Electricity System Marginal Price Forecasting Models Using Statistical and Artificial Intelligence Methods by Mehmet Kızıldağ, Fatih Abut, Mehmet Fatih Akay

“…This study addresses the challenges of SMP prediction in Turkey by proposing a comprehensive forecasting framework that integrates machine learning, deep learning, and statistical models. Advanced feature selection techniques, such as Minimum Redundancy Maximum Relevance (mRMR) and Maximum Likelihood Feature Selector (MLFS), are employed to refine model inputs. …”
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Study of the interval width of features for improving forecasting efficiency of the transport flow intensity by I. N. Pugachev, N. G. Sheshera, D. Yе. Grigorov

“…There are a lot of methods for increasing the accuracy of predictive models, but this method has been used for the first time. Logicaland statistical validity of the selection automation of interval rages are the main feature of this method. …”
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Limitations to Dynamical Error Suppression and Gate-Error Virtualization from Temporally Correlated Nonclassical Noise by Michiel Burgelman, Nattaphong Wonglakhon, Diego N. Bernal-García, Gerardo A. Paz-Silva, Lorenza Viola

“…We explicitly relate these features to the evolution of the bath statistics during the computation, which has not been fully accounted for in existing treatments. …”
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Robust Data-Driven State of Health Estimation of Lithium-Ion Batteries Based on Reconstructed Signals by Byron Alejandro Acuña Acurio, Diana Estefanía Chérrez Barragán, Juan Carlos Rodríguez, Felipe Grijalva, Luiz Carlos Pereira da Silva

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THE COMPARATIVE ANALYSIS AND RESEARCH OF METHODS OF CALCULATION OF LOSSES OF THE ELECTRIC POWER IN THE SYSTEMS OF ELECTRICAL POWER SUPPLY OF THE INDUSTRIAL ENTERPRISES by E. I. Gracheva, I. I. Ilyasov, A. N. Alimova

“…The fields of use of methods of calculation of losses depending on the initial information, the accepted assumptions and the possible expected calculation errors are defined. The key features are revealed and recommendations about application of the probable and determined methods are developed.…”
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Error-Robust Modes of the Retinal Population Code. by Jason S Prentice, Olivier Marre, Mark L Ioffe, Adrianna R Loback, Gašper Tkačik, Michael J Berry

“…We found that the modes represent localized features of the visual stimulus that are distinct from the features represented by single neurons. …”
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Correlated systematic uncertainties and errors-on-errors in measurement combinations with an application to the 7–8 TeV ATLAS–CMS top quark mass combination by Enzo Canonero, Glen Cowan

“…Abstract The Gamma Variance Model is a statistical model that incorporates uncertainties in the assignment of systematic errors (informally called errors-on-errors). …”
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Revisiting Statistical Aspects of Nuclear Material Accounting by T. Burr, M. S. Hamada

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Analysis of errors in endodontic treatment according to cone-beam computed tomography by E. P. Zinkovskaya, E. V. Chestnykh, L. A. Goreva, I. O. Larichkin, N. V. Shedyakova, E. L. Zakharova

“…There is a decrease in the incidence of the most common errors of endodontic treatment. The method of cone beam computed tomography is important at the stage of diagnosis and planning of endodontic treatment to assess the features of the internal structure of the tooth.…”
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Error disclosure: what residents say and what patients find effective by Emily Grossniklaus, Angelo D'Addario, Ann King, Thomas H. Gallagher, Kathleen Mazor, Andrew A. White

“…Utilizing an assessment and feedback system that encourages responders to include themes layperson raters value most and to omit harmful expressions could be an important feature for future software for error disclosure communication training.…”
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Noise-agnostic quantum error mitigation with data augmented neural models by Manwen Liao, Yan Zhu, Giulio Chiribella, Yuxiang Yang

“…Abstract Quantum error mitigation, a data processing technique for recovering the statistics of target processes from their noisy version, is a crucial task for near-term quantum technologies. …”
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