Thermal Regime of Snow Cover in Winter in The High-Mountainous Part of Elbrus According To Observational Data and Modeling Results by E. D. Drozdov, D. V. Turkov, P. A. Toropov, A. Yu. Artamonov

“…The inaccuracy in determining the snow surface roughness parameter, which in high mountain conditions is characterized by significant temporal variability, can contribute to the error.…”
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Estimation of Atmospheric Boundary Layer Turbulence Parameters over the South China Sea Based on Multi-Source Data by Ying Liu, Tao Luo, Kaixuan Yang, Hanjiu Zhang, Liming Zhu, Shiyong Shao, Shengcheng Cui, Xuebing Li, Ningquan Weng

“…Utilizing Monin–Obukhov Similarity Theory (MOST) as the theoretical foundation, the model’s performance is evaluated by comparing its outputs with the observed diurnal cycle of near-surface optical turbulence. Error analysis indicates a root mean square error (RMSE) of less than 1 and a correlation coefficient exceeding 0.6, validating the model’s predictive capability. …”
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Data driven tensile strength prediction for fiber-reinforced rubberized recycled aggregate concrete using machine learning by Avijit Pal, Khondaker Sakil Ahmed, Nur Yazdani

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Enhancing the prediction of irrigation demand for open field vegetable crops in Germany through neural networks, transfer learning, and ensemble models by Samantha Rubo, Jana Zinkernagel

“…Two additional models are pretrained with freely accessible AWC data from 320 stations across Germany and subsequently fine-tuned with the same experimental data as before. …”
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Predictive and Explainable Machine Learning Models for Endocrine, Nutritional, and Metabolic Mortality in Italy Using Geolocalized Pollution Data by Donato Romano, Michele Magarelli, Pierfrancesco Novielli, Domenico Diacono, Pierpaolo Di Bitonto, Nicola Amoroso, Alfonso Monaco, Roberto Bellotti, Sabina Tangaro

“…Performance was assessed using metrics such as coefficient of determination (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>r</mi><mn>2</mn></msup></semantics></math></inline-formula>), mean absolute error (MAE), and root mean squared error (RMSE), revealing that GB outperformed both RF and XGB, offering superior predictive accuracy and model stability (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>r</mi><mn>2</mn></msup></semantics></math></inline-formula> = 0.55, MAE = 0.17, and RMSE = 0.05). …”
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Using Microsoft Excel for Lesson Transcript Analysis by Gereltuya Tsereljav, Ganbaatar Tumurbaatar, Jadamba Badrakh, Battogtokh Tsiyen-Oidov

“…In the future, the application of this digital tool can be expanded to enhance the outcomes of lesson analysis, facilitate lesson sensitivity analysis, and support the development of data-driven approaches in educational research.…”
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APPLICATION OF NON-TEST METHODS FOR CHANEL ESTIMATION by M. L. Maslakov, M. S. Smal

“…The possibilities  of increasing  the data  rate of adaptive HF communication systems by reducing  redundancy in the  form  of test  signals  used  for its operation are  considered. …”
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A prediction model of massive 5G network users’ revisit behavior based on telecom big data by Yudi SUN

“…Users in 5G networks will generate a large amount of access data, which makes it difficult to accurately predict users’ revisit behavior.Therefore, a prediction model of massive 5G network users’ revisit behavior based on telecom big data was proposed.The user’s historical online behavior characteristic data was extracted from the telecom big data to build a data set.Multi order weighted Markov chain model was introduced.The model weight value was obtained by calculating the autocorrelation coefficient of each order, and the statistics of the model were calculated.After analysis, the one-step transition probability matrix of Markov chain with each step size was obtained, so as to accurately predict the revisit behavior of massive users in 5G network.The experimental results show that the proposed model has the lowest mean error and standard deviation, as well as the highest accuracy, recall, precision and F1 indicators, which can prove that the proposed method has a very obvious advantage in predicting users’ revisit behavior.…”
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A prediction model of massive 5G network users’ revisit behavior based on telecom big data by Yudi SUN

“…Users in 5G networks will generate a large amount of access data, which makes it difficult to accurately predict users’ revisit behavior.Therefore, a prediction model of massive 5G network users’ revisit behavior based on telecom big data was proposed.The user’s historical online behavior characteristic data was extracted from the telecom big data to build a data set.Multi order weighted Markov chain model was introduced.The model weight value was obtained by calculating the autocorrelation coefficient of each order, and the statistics of the model were calculated.After analysis, the one-step transition probability matrix of Markov chain with each step size was obtained, so as to accurately predict the revisit behavior of massive users in 5G network.The experimental results show that the proposed model has the lowest mean error and standard deviation, as well as the highest accuracy, recall, precision and F1 indicators, which can prove that the proposed method has a very obvious advantage in predicting users’ revisit behavior.…”
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A Data and Machine Learning-Based Approach for the Conversion of the Encounter Wave Frequency Spectrum to the Original Wave Spectrum by JeongYong Park, MooHyun Kim

“…The results demonstrate that the ANN model can effectively predict the original wave spectrum with high accuracy, as evidenced by a favorable R2 value and error distribution analysis. This approach not only enhances the reliability of wave spectrum estimation during maritime navigation but also broadens the capability of real-time operational controls and adjustments.…”
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Methodology for detecting anomalies in cyber attack assessment data using Random Forest and Gradient Boosting in machine learning by A. S. Kechedzhiev, O. L. Tsvetkova, A. I. Dubrovina

“…The research aims to detect anomalies in data using machine learning models, in particular random forest and gradient boosting, to analyze network activity and detect cyberattacks. …”
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A comparative study of machine learning classifiers for intelligent fault diagnosis of electric vehicles based on FMECA data by Leila Boucerredj, Nadir Benalia

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Air Traffic Trends and UAV Safety: Leveraging Automatic Dependent Surveillance–Broadcast Data for Predictive Risk Mitigation by Prasad Pothana, Paul Snyder, Sreejith Vidhyadharan, Michael Ullrich, Jack Thornby

“…The statistical analysis findings highlight distinct traffic patterns across airport classes, emphasizing the practicality of utilizing ADS-B data for UAV flight scheduling to minimize conflicts with manned aircraft. …”
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Mixed Layer Depth Estimation From Multisource Remote Sensing Data Using Clustering-Machine Learning Method by Zengxin Guan, Kaijun Ren, Senliang Bao, Hengqian Yan, Huizan Wang, Yanlai Zhao, Jianbin Liu

“…The estimation error (RMSE) was less than 11.2 m. To assess practical applicability, comparative experiments using remote sensing data were performed, highlighting the model's feasibility and utility. …”
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A targeted one dimensional fully convolutional autoencoder network for intelligent compression of magnetic flux leakage data by Wenbo Xuan, Pengchao Chen, Rui Li, Fuxiang Wang, Kuan Fu, Zhitao Wen

“…Through practical experimental analysis, the reconstruction error such as MAE is reduced by about 27.7% and the compression ratio is improved by about 14% compared with traditional methods such as PCA. …”
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Unravelling urban carbon dynamics: a multi-source data study on Nanchang city's carbon dioxide emissions by Lingyun Yao, Li Wang, Ke Wang, Zheng Niu

“…Urban CO2 emissions constitute a significant proportion of the total global emissions, and the analysis of urban CO2 emissions typically requires the availability of baseline data with high spatiotemporal resolution. …”
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Estimation of Summer Air Temperature over China Using Himawari-8 AHI and Numerical Weather Prediction Data by Hailei Liu, Qi Zhou, Shenglan Zhang, Xiaobo Deng

“…This study proposed an instantaneous summer air temperature (Tair) estimation model using the Himawari-8 Advanced Himawari Imager (AHI) brightness temperatures (BTs) in split-window channels and other auxiliary data. Correlation analysis and stepwise linear regression were used to select the predictors for Tair estimation. …”
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Data driven fuel consumption prediction model for green aviation using radial basis function neural network by Yuandi Zhao, Zhongyi Wang, Xiaohui Wang, Ye Song, Yuzhe Han

“…The error variances from ten-fold cross-validation were 0.31%, 0.15%, and 0.29%, respectively, confirming the robustness of the model. …”
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Rapid Probabilistic Inundation Mapping Using Local Thresholds and Sentinel-1 SAR Data on Google Earth Engine by Jiayong Liang, Desheng Liu, Lihan Feng, Kangning Huang

“…The performance metrics—Brier Scores (0.05–0.07), Logarithmic Loss (0.1–0.2), Expected Calibration Error (0.03–0.04), and Reliability Diagrams—demonstrated reliable accuracy. …”
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ABOUT ACCURACY OF SYNCHRONOUS TIME IN UPS OF RUSSIA by Y. Kononov, I. Levchenko, E. Satsuk, D. Tuchina

“…A description is given of experiments based on recording deviations of the clock, clocked by the voltage signal of the power network, and on the analysis of data from phasor measuring unit. A constant average daily average lag of synchronous time from astronomical time of 1 s was revealed, this is due to the systematic error of frequency sensors. …”
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