On the reliability of published findings using the regression discontinuity design in political science by Drew Stommes, P. M. Aronow, Fredrik Sävje

“…The distribution of published results exhibits pathological features; estimates tend to bunch just above the conventional level of statistical significance. …”
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Accurate modeling of annular gas-water flow across diverse inclination angles using an advanced drift-flux correlation by Abdulaziz AlSaif, Abdelsalam Al-Sarkhi

“…To assess its generalizability, the proposed correlation was tested on blind experimental datasets featuring pipe diameters three times larger than those used during development, where it attained the lowest average error of 0.7 %. …”
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An effective imputation approach for handling missing data using intuitionistic fuzzy clustering algorithms by Kavita Sethia, Jaspreeti Singh, Anjana Gosain

“…Experimental analysis and statistical analysis (Friedman Test) on four UCI datasets, using two performance metrics, Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), demonstrate that the proposed algorithms consistently outperform eight existing fuzzy clustering-based MDI algorithms.…”
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Graph theoretic and machine learning approaches in molecular property prediction of bladder cancer therapeutics by Huiling Qin, Atef F. Hashem, Muhammad Farhan Hanif, Osman Abubakar Fiidow

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Forecasting the daily evaporation by coupling the ensemble deep learning models with meta-heuristic algorithms and data pre-processing in dryland by Tonglin Fu, Dong Wang, Jing Jin

“…The estimating performance was evaluated by using the statistical accuracy metrics, including R2, the mean squared error (MSE), the mean absolute error (MAE), the root mean squared error (RMSE), and the mean absolute percentage error (MAPE). …”
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Predicting Wastewater Characteristics Using Artificial Neural Network and Machine Learning Methods for Enhanced Operation of Oxidation Ditch by Igor Gulshin, Nikolay Makisha

“…Evaluation metrics (Mean Squared Error (MSE), Mean Absolute Error (MAE), Symmetric Mean Absolute Percentage Error (SMAPE), and Coefficient of Determination (R²)) indicated similar performance across models, with ARIMA–LSTM yielding the best results. …”
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An evaluation of multi-fidelity methods for quantifying uncertainty in projections of ice-sheet mass change by J. D. Jakeman, M. Perego, D. T. Seidl, T. A. Hartland, T. R. Hillebrand, M. J. Hoffman, S. F. Price

“…When quantifying uncertainty introduced by a high-dimensional parameterization of the basal friction field, MFSE was able to reduce the mean-squared error in the estimates of the statistics by well over an order of magnitude when compared to a single-fidelity approach that only used the highest-fidelity model. …”
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Ultra-short-term Multi-region Power Load Forecasting Based on Spearman-GCN-GRU Model by Junying WU, Xin LU, Hong LIU, Bin ZHANG, Shouliang CHAI, Yunchun LIU, Jianan WANG

“…In terms of prediction accuracy, the Spearman-GCN-GRU model are optimal in common evaluation indexes compared with traditional statistical models and neural network models. Specifically, in terms of the root mean square error (RMSE), the Spearman-GCN-GRU model exhibits a respective decrease of 13.90%, 11.66%, and 8.36% compared to the GRU, GCN and deep neural network (DNN) models, demonstrating its superior predictive performance.…”
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Determination of Spatiotemporal Gait Parameters Using a Smartphone’s IMU in the Pocket: Threshold-Based and Deep Learning Approaches by Seunghee Lee, Changeon Park, Eunho Ha, Jiseon Hong, Sung Hoon Kim, Youngho Kim

“…The proposed method achieved error rates in event detection below 2% of the gait cycle for healthy gait and a maximum of 4.4% for patient gait in event detection, with corresponding parameter estimation errors also within 4%. …”
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Development of biometric systems for passenger identification based on noise-resistant coding means by A. A. Gladkikh, A. K. Volkov, T. G. Ulasyuk

“…The purpose of the article is to analyze the techniques of enhancing reliability of various biometric identification facilities by means of using error correction codes. The basic elements and the principle of the classical biometric system functioning are presented. …”
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An interpretable stacking machine-learning model to predict the hot torsion flow characteristics of a micro-alloyed steel by Hojjat Emami, Mehdi Shaban Ghazani

“…Feature importance analysis using Shapley additive explanations revealed that temperature and strain are the most influential features affecting the target stress value. …”
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Clinicopathological spectrum of central nervous system germ cell tumors: A single-institution retrospective study by Sreelekha Appasani, Neelima Radhakrishnan, Anitha Mathews

“…Aims: To determine the clinicopathological features of CNSGCTs diagnosed at our center. Settings and Design: A retrospective study of all histologically diagnosed CNSGCTs, during 2006-2019. …”
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Hybrid transfer learning and self-attention framework for robust MRI-based brain tumor classification by Soumyarashmi Panigrahi, Dibya Ranjan Das Adhikary, Binod Kumar Pattanayak

“…Magnetic Resonance Imaging (MRI) is crucial for diagnosis, but manual analysis is resource-intensive and error-prone, highlighting the need for robust Computer-Aided Diagnosis (CAD) systems. …”
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Impact of Situation Awareness Variations on Multimodal Physiological Responses in High-Speed Train Driving by Wenli Dong, Weining Fang, Hanzhao Qiu, Haifeng Bao

“…Results: The results of statistical analysis confirmed the effectiveness of this paradigm in inducing SA level changes, revealing significant relationships between SA levels and key physiological metrics, including eye movement patterns, ECG features (e.g., heart rate variability), and EEG power spectral density across theta, alpha, and beta bands. …”
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Development of a Fire Risk Assessment Program for Submerged Tunnels by Suk-Min Kong, Hyo-Gyu Kim, Ho-Hyeong Lee, Seong-Won Lee

“…As a result, the total risk error rate was 0.4% for road tunnels and within 5.0% for railway tunnels, showing similar levels of results. …”
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USING THE ANYLOGIC ENVIRONMENT FOR MODELING AND ANALYSIS OF THE INFORMATION SECURITY AUDIT PROCESS by Kamilla M. Khuranova, Igor D. Kologorov, Sergey A. Reznichenko, Leonid N. Kessarinskiy

“…Input data from RTM Group includes statistical timelines for audit stages and incident frequency patterns. …”
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Machine learning models for accurately predicting properties of CsPbCl3 Perovskite quantum dots by Mehmet Sıddık Çadırcı, Musa Çadırcı

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Solar Energy Datasets of Deep Learning Models Incorporating with GK-2A and ASOS Ground Measurements by Jong-Sung Ha, Seungtaek Jeong, Seyun Min, Yejin Lee, Suhwan Kim, Doehee Han, Jong-Min Yeom

“…The BPNN deep learning model achieved a statistical accuracy of root mean squared error (RMSE) 77.32 Wm-2, mean bias error (MBE) -0.48 Wm-2, and R2 0.91, indicating high accuracy. …”
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Efficient multi-station air quality prediction in Delhi with wavelet and optimization-based models. by Lakshmi Sankar, Krishnamoorthy Arasu

“…The Wilcoxon Signed-Rank Test statistically validated the relevance of the suggested feature extraction and selection method for all monitoring stations. …”
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Prediction of the Residual Compressive Strength of Rice Husk Ash Concrete after Exposure to Elevated Temperatures Using XGBoost Machine Learning Algorithm by Elvis Ang'ang'o, Silvester Abuodha, Siphila Mumenya

“…The model accuracy was checked using statistical scores: coefficient of determination (R2), root mean squared error (RMSE), and mean absolute error (MAE). …”
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