Evaluating the accuracy of GNSS measurements in RTK mode using commercial and non-commercial correction network by D.P. Pobihailo, A.K. Kamburov, D.V. Mitchenko

“…The 1Yocto and EUREF networks showed worse results, and EGNOS was the least suitable for high-precision geodesy due to significant vertical and plan errors. Additionally, the results of PPK (Post-Processed Kinematic) were analysed, but due to the short duration of fixation, these data are considered only indicative. …”
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Application of deep reinforcement learning in parameter optimization and refinement of turbulence models by Zhan Zhang

“…Numerical simulation of complex building wind fields was achieved using OpenFOAM software, and sensitivity analysis of model parameters was conducted. Key parameters that significantly affected simulation results were identified, and GPR (Gaussian Process Regression) was established as a surrogate model to fit the initial CFD (Computational Fluid Dynamics) simulation data. …”
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Explainable AI-driven assessment of hydro climatic interactions shaping river discharge dynamics in a monsoonal basin by Prashant Parasar, Akhouri Pramod Krishna

“…The main findings of this study are (1) KAN demonstrated high predictive performance with root mean squared error (RMSE) values ranging from 42.7 to 58.3 m3/s, Nash–Sutcliffe efficiency (NSE) between 0.80 and 0.87, mean absolute error (MAE) between 28.9 to 52.7 and R2 values between 0.84 and 0.90 across stations. (2) SHAP based feature contribution analysis identified Relative humidity (hurs), specific humidity (huss), and temperature (tas) as key predictors, while (pr) showed limited contribution due to spatial inherent inconsistencies in GCM precipitation data. (3) The bootstrapped SHAP distributions highlighted substantial variability in feature importance, particularly for humidity variables, revealing station specific uncertainty patterns in model interpretation. (4) The KAN framework results indicate strong temporal alignment and physical realism, confirming KAN’s robustness in capturing seasonal discharge dynamics and extreme flow events under monsoon influence environments. (5) In this study KAN with SHAP (SHapley additive exPlanations) is implemented for hydrological modeling under monsoon-influenced and data-limited regions such as SRB, offering improved accuracy, functional precision and efficiency compared to traditional models. …”
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A comparative study of machine learning in predicting the mechanical properties of the deposited AA6061 alloys via additive friction stir deposition by Qian Qiao, Quan Liu, Jiong Pu, Haixia Shi, Wenxiao Li, Zhixiong Zhu, Dawei Guo, Hongchang Qian, Dawei Zhang, Xiaogang Li, C. T. Kwok, L. M. Tam

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Method to Interpret Injection Profile of Water Injection Well Based on DTS by ZHANG Xiliang, LI Ang, WU Jie, WANG Lijun, ZUO Kai, ZHANG Zhihu

“…A simulated annealing (SA) algorithm is used to establish a DTS data inversion model for water injection wells. Using the inversion model, the DTS data of a field water injection well has been inverted and accurate water injection profile is obtained. …”
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Algorithm of the automated control of displacement of the load overall center of gravity of railroad car for railway transportation by Tat’yana A. Vinokurova, Andrey B. Galushkin, Anatoliy A. Rakhmilevich

“…The algorithm is proposed for the automated control of displacement of the load overall center of gravity relative to transversal and/or longitudinal planes of symmetry of the car during railway transportation and decision-making about availability of commercial damages upon results of the car wheel weighting under way by the weighting equipment in cluded in the set of equipment of the systems of the automated commercial visual inspection of trains and cars. The baseline data for calculations includes results of the car wheel weighting and relative error of measurement of the loads with scale set during certification, and the car container weight accepted according to the data of the car in the Automated database of the freight car fleet of JSC “RZD”. …”
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An Intelligent Crow Search Optimization and Bi-GRU for Forest Fire Detection System Using Internet of Things by Syed Abdul Moeed, Bellam Surendra Babu, M. Sreevani, B. V. Devendra Rao, R. Raja Kumar and Gouse Baig Mohammed

“…CSO is inspired by the intelligent foraging behavior of crows, and when combined with fractional calculus, it provides a robust optimization framework that improves the accuracy and efficiency of the AI model. Experimental analysis shows that the proposed technique outperformed the other existing traditional approaches with an accuracy of 99.32% and an error rate of 0.12%. …”
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Machine learning based prediction of geotechnical parameters affecting slope stability in open-pit iron ore mines in high precipitation zone by John Gladious, Partha Sarathi Paul, Manas Mukhopadhyay

“…To quantify these impacts, the system calculates the percentage changes in these properties. A robust Exploratory Data Analysis (EDA) was conducted to elucidate the distributions of pre-monsoon and post-monsoon properties and uncover interrela- tionships among them. …”
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A Hybrid Machine Learning Approach for High-Accuracy Energy Consumption Prediction Using Indoor Environmental Quality Sensors by Bibars Amangeldy, Nurdaulet Tasmurzayev, Timur Imankulov, Baglan Imanbek, Waldemar Wójcik, Yedil Nurakhov

“…These findings highlight the value of integrating high-resolution IEQ data into predictive frameworks and demonstrate that such data can significantly improve forecasting accuracy. …”
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Comprehensive Uncertainty Quantification in Nuclear Safeguards by E. Bonner, T. Burr, T. Krieger, K. Martin, C. Norman

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Modeling Concrete Compressive Strength Variation with Age Using Origin Pro Software. by Nabaweesi, Alice

“…Origin Pro software, a powerful data analysis tool, was employed to analyze existing data and develop the model. …”
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A Novel Framework for Road Information Extraction From Low-Cost MMS Point Clouds by Baris Suleymanoglu, Metin Soycan

“…Comparative analysis revealed that the proposed approach outperformed conventional image-based methods, with an average deviation of 2.9 cm from reference data in lane line detection. …”
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Spherical assembly model and prediction of pressure loss of porous media flow in goaf by Fangxing CHEN, Xiaoli HAO, Shiqiang CHEN, Yiqun LI, Shilin LI, Yi LU

“…The reliability of the numerical simulation results was verified through dimensionless analysis using experimental data from porous media with a porosity of 0.375. …”
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Determination of the positioning accuracy of industrial manipulators using a digital photo/video camera by N. V. Norkina, F. A. Mevis

“…The aim of the study is to develop a system for the analysis and evaluation of positioning errors manipulators precision industrial robots used in the production of microelectronic equipment. …”
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The Effect of Islamic Work Ethic and Islamic Work Culture to Enhance Productivity of Batik Worker during Pandemic COVID-19 by Susminingsih Susminingsih, Rinda Asytuti, Wahidatul Maghfiroh, Munip Munip

“…Determination of sample size in this research using the Slovin formula with a margin error of 5 %. Data collection was done through survey techniques. …”
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Short-term prediction of regional energy consumption by metaheuristic optimized deep learning models by Ngoc-Quang Nguyen, Phuong-Thao-Nguyen Nguyen, Quynh-Chau Truong

“…This study proposed a hybrid deep learning model, called I-CNN-JS, by incorporating a jellyfish search (JS) algorithm into an ImageNet-winning convolutional neural network (I-CNN) to predict week-ahead energy consumption. First, numerical data were encoded into grayscale images for input of the proposed model, showcasing the novelty of using image data for analysis. …”
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A Survey of Machine Learning Methods for Time Series Prediction by Timothy Hall, Khaled Rasheed

“…A detailed analysis of the most used error metrics and hyperparameter tuning methods in the reviewed papers is included. …”
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SPECIFICS OF DIGITAL FILTERING MODELING FOR PRODUCTIVE DEPOSITS (BY EXAMPLE OF THE KOSHEKHABLSKOYE FIELD) by R. A. Gasumov, E. R. Gasumov

“…The necessary initial data for the calculation were obtained from the results of the geological and geophysical studies and field data analysis. …”
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A novel hybrid CLARA and fuzzy time series Markov chain model for predicting air pollution in Jakarta by Nurtiti Sunusi, Ankaz As Sikib, Sumanta Pasari

“…A 5 × 5 Markov transition probability matrix was subsequently constructed for modeling the data. Predicted values for SO₂ and CO in Jakarta using the CLARA-FTSMC hybrid method showed strong alignment with the actual data. …”
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Corrosion Predictive Model in Hot-Dip Galvanized Steel Buried in Soil by Lorena-De Arriba-Rodríguez, Francisco Ortega-Fernández, Joaquín M. Villanueva-Balsera, Vicente Rodríguez-Montequín

“…Specifically, the mean square error was 290.6 g/m2 (range of the objective variable is from 51.787 g/m2 to 5950.5 g/m2), R2 was 0.96, and from a relative error of 0.14, the success of the estimate was 100%. …”
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