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

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STATE PREDICTION OF WIND TURBINE GENERATOR BASED ON K-CNN AND N-GRU (MT) by CHAI Tong, YUAN YiPing, MA JunYan, FAN PanPan

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Post-stroke lesion correlates of errors in verbal and spatial production tasks by Antonino Visalli, Antonino Visalli, Natasha Maldonado, Mete Dadak, Heinrich Lanfermann, Karin Weißenborn, Bruno Kopp

“…Furthermore, the error-monitoring hypothesis predicts domain-incongruent specialization, with left hemisphere dominance for spatial and right hemisphere dominance for verbal errors.MethodsWe performed voxel-based lesion-behavior mapping in N = 110 acute stroke patients who completed a cognitively demanding, error-prone, five-point spatial design fluency task and a verbal word-fragment completion task.ResultsSignificant associations were found between lesion location and error rates in both tasks, spatial fluency (correlation = 0.36, p < 0.001) and verbal completion (correlation = 0.31, p = 0.001). …”
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Selection of geometrical features of nuclei оn fluorescent images of cancer cells by Ya. U. Lisitsa, M. M. Yatskou, V. V. Skakun, P. D. Pavel D. Kryvasheyeu, V. V. Apanasovich

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Algorithm of Assessing Dynamic Correlation between Time Series Connected by TVP-Regression Model by N. A. Moiseev, G. V. Aivazian

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Research on the prediction model of gas emission based on grey system theory by Liyang Bai, Hui Geng, Guangming Yu

“…The related factors with a grey correlation degree greater than 0.7, from largest to smallest, are: coal seam gas content X1 > coal seam thickness X3 > mining intensity X11 > coal seam depth X2 > adjacent gas content X8.Combined with the field measured data, three grey prediction models for predicting gas emission are determined.After a posterior difference test, the accuracy of GM (0,12) model is excellent.By comparing the predicted data of the model with the actual data, it shows that the GM (0,N) model has good forecasting results.At the same time, in order to prove the advantages of GM (0,N) model, the prediction results are compared with those of multiple linear regression model.The prediction results of GM (0,N) model and multiple linear regression model are compared.The prediction results show that the relative error of GM (0,12) model is 0.799%,the relative error of multiple linear regression model is 3.643%.It shows that GM (0,12) model can better predict gas emission.…”
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Monte Carlo optimization-based QSAR modeling of Staphylococcus aureus inhibitory activity of coumarin-1,2,3-triazole hybrids by Mishra Krishna N., Upadhyay Harish C., Verma Poonam

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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

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APPLICATION OF STATISTICAL METHODS TO ASSESS THE METROLOGICAL CHARACTERISTICS OF RADIO-HOLOGRAPHIC MEASURING COMPLEXES by A. P. Grinchuk, A. G. Buday, A. V. Gromyko

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Updating Measures of CME Arrival Time Errors by C. Kay, E. Palmerio, P. Riley, M. L. Mays, T. Nieves‐Chinchilla, M. Romano, Y. M. Collado‐Vega, C. Wiegand, A. Chulaki

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Predicting yellow mosaic disease severity in yardlong bean using visible imaging coupled with machine learning model by Abhishek Kumar Dubey, Prakash Kumar Jha, Kumari Shubha, RN Singh, Manisha Tamta, Sonam Sah, Santosh Kumar, Sanjeev Kumar, Rakesh Kumar, Kirti Saurabh, Rajeev Kumar, Anup Das, P. V. V. Prasad, Arbind Kumar Choudhary

“…Out of 143 genotypes screened based on final disease severity 3, 18, 18, 17, 34 and 53 genotypes were grouped in immune, resistant, moderately resistant, moderately susceptible, susceptible and highly susceptible categories, respectively. Model performances was evaluated using R2, d-index, mean bias error, and normalized Root Mean Square Error (n-RMSE) metrics. …”
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Validation of the exercise-related cognitive errors questionnaire short form by Sean R Locke, James Sessford, Mary E Jung

“…Exploratory factor analysis on datasets 1 ( N = 394), 2 ( N = 177), and 3 ( N = 1027) suggested that a seven-item, one-factor model fit the data. …”
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Color-Based Lifetime Estimation of LEDs Using Spectral Power Distribution Prediction Through Analytical and Machine Learning Models by J. Lokesh, Savitha G. Kini, M. G. Mahesha, Anjan N. Padmasali

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Information-Theoretic Security of RIS-Aided MISO System Under <i>N</i>-Wave with Diffuse Power Fading Model by José David Vega-Sánchez, Ana Zambrano, Ricardo Mena, José Oscullo

“…For the numerical results, the secrecy outage probability, the average secrecy rate, and the average secrecy loss under different setups of RIS-aided wireless systems are assessed by varying the fading parameters of the <i>N</i>-wave with a diffuse power fading channel model. …”
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BiLSTM- and GNN-Based Spatiotemporal Traffic Flow Forecasting with Correlated Weather Data by Abdullah Alourani, Farzeen Ashfaq, N. Z. Jhanjhi, Navid Ali Khan

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Predicting classification errors using NLP-based machine learning algorithms and expert opinions by Peiheng Gao, Chen Yang, Ning Sun, Ričardas Zitikis

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Spatially and temporally correlated channel estimation and detection for comparator network-aided MIMO receivers with 1-bit ADCs by Luiz Sampaio, Lukas T. N. Landau

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Clinical assessment of the changes in corneal endothelium cell density, IOP and correlated factors after phakic iol implantation by A.F. Yusupov, M.Kh. Karimova, N.N. Zaynutdinov, Kh. M. Kamilov, M.S. Kasimova

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Exploring the impact of interaction dynamics and professional capacity and development on cognitive medical errors: a multiple-case study of healthcare professionals in Jordan by Heba Hijazi, Mohammad S. Alyahya, Main N. Alolayyan, Farah Ajayneh, Rabah Al Abdi, Ahmed Hossain, Alounoud AlMarzooqi, Mohamad Alameddine

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Prediction of drop size distribution and mean drop size in an L-shaped pulsed packed column using artificial neural network (ANN) model and semi-empirical correlation by Ali Ravandeh, Sajad Khooshechin

“…Moreover, the ANN model significantly reduced the maximum prediction error in drop size distribution, particularly under conditions where the semi-empirical model showed poor performance. …”
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