Arithmetic on Your Phone: A Large Scale Investigation of Simple Additions and Multiplications. by Federico Zimmerman, Diego Shalom, Pablo A Gonzalez, Juan Manuel Garrido, Facundo Alvarez Heduan, Stanislas Dehaene, Mariano Sigman, Andres Rieznik

“…Our results confirm and replicate, on a large sample, six of the main principles derived in a long tradition of investigation: size effect, tie effect, size-tie interaction effect, five-effect, RTs and error rates correlation effect, and most common error effect. …”
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An improved algorithm for prediction of vehicle trajectories using short-term goal-driven network by Abdalla Tawfik, Zaki Nossair, Roaa Mubarak

“…STG demonstrated a substantial improvement of over 6% in average displacement error and more than 8% in final displacement error. …”
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New Measurement Methods for Transition Resistance of Rail Tracks in Urban Rail Transit by LI Shichen, YU Xianhu, LI Jinglong

“…The measurement results by the traditional DC method and the impulse current method are analyzed and compared. [Result & Conclusion] As the rail track transition resistance increases, the measurement error of the impulse current method gradually decreases and remains within 5%. …”
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Assessment of a simulation-based implementation method to support the introduction of a new invasive device: a prospective cross-over study by Pierre Albaladejo, Julien Picard, Pierrick Bedouch, Claire Chapuis, Cordélia Salomez-Ihl

“…Simulation training has also led to a better understanding of the most common errors. Because of new IABPS widespread use, it was all the more important to prevent usage errors.…”
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Componentes de dificultad de tareas de razonamiento deductivo aplicando el modelo LLTM de Fischer by María Silvia Galibert, Facundo Abal, Sofía Auné, Gabriela Susana Lozzia, María Ester Aguerri

“…El análisis de las fuentes de error en la resolución de silogismos originó, den - tro de la psicología cognitiva, explicaciones como el efecto atmósfera, el sesgo de la figura y la conver - sión ilícita. …”
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A hybrid deep learning framework for regional reference crop evapotranspiration estimation in the Hetao Irrigation District using limited meteorological data by Xiao Zhang, Yuxin Tao, Chenglong Zhang

“…New hydrological insights for the region: The results indicated that the mass transfer-based CNN-Informer3 model achieved the best estimation performance, with its average Determination Coefficient (R2), Nash-Sutcliffe efficiency coefficient (NSE), Root Mean Square Error (RMSE), Relative Root Mean Square Error (RRMSE), Mean Absolute Error (MAE) and Percentage Bias (PBIAS) of 0.9712, 0.9665, 0.3764 mm/d, 0.1192, 0.2591 mm/d and -3.8143 % for accuracy evaluation, significantly outperforming other models. …”
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Image segmentation method of plastic-film corn canopy by ZHANG Wanhong, LIU Wenzhao

“…The low RMSE and error rate further confirmed the rationality of the method used in this paper.In conclusion, the method presented in this paper for image segmentation of plastic-film corn canopy is reliable under the weak light condition.…”
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A hybrid optimization algorithm based on cascaded (1 + PI)-PI-PID controller for load frequency control in interconnected power systems by Md. Shahid Iqbal, Md. Faiyaj Ahmed Limon, Md. Monirul Kabir, Md. Zakir Hossain, Md. Fahad Jubayer, Md. Janibul Alam Soeb

“…The study uses integral absolute error (IAE), integral square error (ISE), integral time absolute error (ITAE), and integral time square error (ITSE) as objective functions. …”
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Long-term forecast of monthly reference evapotranspiration of the period 2018-2027 using SARIMA and GRNN models (case study: Rasht synoptic station) by Pouya Aghelpoor, Vahid Varshavian, Mehraneh Khodamoradpoor

“…Other SARIMA operators, including seasonal and non-seasonal autoregressive, and average seasonal and non-seasonal moving, were selected by trial and error. Optimization of the GRNN model was accomplished by trying and error of the spread parameter. …”
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Development of an upper limb muscle strength rehabilitation assessment system using particle swarm optimisation by Chuangan Zhou, Siqi Wang, Meiyi Wu, Wei Lai, Junyu Yao, Xingyue Gou, Hui Ye, Jun Yi, Dong Cao

“…Model performance was evaluated using R-squared (R2), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Bias Error (MBE).ResultsThe system successfully collected electromyographic and kinematic data. …”
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The neural network for measuring IOP by Maklakov method: comparison between neuronal net and experts by A.A. Rascheskov, I.A. Frolychev, N.A. Pozdeeva

“…The accuracy of AI-Tonometry: the mean absolute error (MAE) is 2.5 mmHg, the mean squared error (MSE) is 8.76, the root mean squared error (RMSE) is 2.96. …”
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Exploring the achievements and forecasting of SDG 3 using machine learning algorithms: Bangladesh perspective. by Md Maeen Molla, Md Sifat Hossain, Md Ayub Ali, Md Raqibul Islam, Mst Papia Sultana, Dulal Chandra Roy

“…<h4>Results</h4>ENET demonstrates superior performance compared to both BRNN and ARIMA in the context of NMR, achieving a Root Mean Absolute Error (RMAE) of 0.603446 and a Root Mean Square Error (RMSE) of 0.451162. …”
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Exploring the potential of deep learning models integrating transformer and LSTM in predicting blood glucose levels for T1D patients by Xin Xiong, XinLiang Yang, Yunying Cai, Yuxin Xue, JianFeng He, Heng Su

“…The model's performance is validated using real-world clinical data and error grid analysis. Results On clinical data, the model achieved root mean square error/mean absolute error of 10.157/6.377 (30-min), 10.645/6.417 (60-min), 13.537/7.283 (90-min), and 13.986/6.986 (120-min). …”
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PRONAME: a user-friendly pipeline to process long-read nanopore metabarcoding data by generating high-quality consensus sequences by Benjamin Dubois, Mathieu Delitte, Salomé Lengrand, Claude Bragard, Anne Legrève, Frédéric Debode

“…By integrating Nanopore-specific quality filtering, clustering and error correction, PRONAME produces high-precision consensus sequences. …”
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Assessing the accuracy of a machine learning prediction for 2 different shoulder prostheses: an external validation study by Gianluca Caprili, MD, Andrea G. Calamita, MD, Michele Novi, MD, Domenico A. Campanacci, MD, PhD, Simone Nicoletti, MD

“…In terms of MAE, we found a higher error than the internal validation only for forward elevation at 3-6 months in group 2 and a lower error in all the other outcome measures at all time points for both groups. …”
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Prediction model for China's monthly LNG ex-factory prices based on BP-ARIMA by Xueping DU, Qinghua ZHAO, Lin MI, Zhikai LANG, Menglin LIU, Jiangtao WU

“…By dynamically adjusting the weight ratio between the two, it significantly enhanced the accuracy of LNG ex-factory price predictions. The mean absolute error (MAE), mean absolute percentage error (MAPE), and root-mean-square error (RMSE) were RMB 188.7/t, 4.1%, and RMB 280.5/t, respectively. …”
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The Precision of Calculating the Optical Power of the IOL in Various Methods of Out-Bag IOL Fixation by Yu. N. Yusef, A. S. Vvedenskiy, M. N. Ivanov, L. Alkharki, N. D. Fokina

“…Three months after surgery there were no significant differences in best corrected visual acuity and the degree of induced astigmatism. Conclusion. Retropupillary implantation of an iris-claw lens during complicated phacoemulsification significantly increases visual acuity without correction, frequency calculation precision within ±0.5 diopters, significantly reduces the average absolute calculation error compared to transscleral suture fixation of the IOL.…”
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A Biometric Comparison Between Myopic and Non-myopic Eyes Treated for Retinopathy of Prematurity by Kaveh Abri Aghdam, Samira Chaibakhsh, Nazanin Hasani, Vahid Zare Hosseinabadi, Ali Aghajani

“…In the non-myopic group, changes in refractive error were solely related to changes in axial length (P = 0.003). …”
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Modification of Epidemiological Model for Predicting the Development of a Socially Significant Infection (by the Example of Chronic Viral Hepatitis C) by R. A. Yakhina

“…As a result of the coronavirus pandemic in 2020, the error was more than 166%, this is due to a decrease in contacts between people and, as a result, a sharp decrease in the incidence of chronic hepatitis C.Conclusion. …”
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Prediction of particulate matter PM2.5 level in the air of Islamabad, Pakistan by using machine learning and deep learning approaches by Muhammad Waqas, Shahid Noor Jan, Basir Ullah, Afed Ullah Khan, Ateeq Ur Rauf, Bakht Niaz Khan

“…Each model's performance was assessed by using statistical indicators including coefficient of determination (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Relative Root Mean Square Error (RRMSE). …”
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