Research on Adaptive Identification Technology for Rolling Bearing Performance Degradation Based on Vibration–Temperature Fusion by Zhenghui Li, Lixia Ying, Liwei Zhan, Shi Zhuo, Hui Li, Xiaofeng Bai

“…Finally, a dual-criteria adaptive bottom-up merging algorithm (DC-ABUM) was proposed, which achieves bearing life-stage identification through error threshold constraints and the adaptive optimization of segmentation quantities. …”
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EVALUATION OF ITERATIVE ALGORITHMS FOR TOMOGRAPHY IMAGE RECONSTRUCTION by Alexandre F. Velo, Alexandre G. Alvarez, Margarida Mizue Hamada, Carlos Henrique de Mesquita

“…The analyses involved the measurement of the contrast to noise ratio (CNR), the root mean square error (RMSE) and the Modulation Transfer Function (MTF),in order to know which algorithm fits the conditions to optimize the system better.   …”
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Influence of Geometrical Design on Defect Formation of Commercial Al-Si-Cu-Mg Alloy Fabricated by High-Pressure Diecasting: Structural Observation and Simulation Validation by Warda Bahanan, Siti Fatimah, Dong-Ju Kim, I Putu Widiantara, Jee-Hyun Kang, Young Gun Ko

“…Because the associated work primarily relies on technical experience, it is necessary to perform the structural analysis of the HPDed component in comparison with simulation-based findings that forecast flow behavior, hence reducing trial and error for optimization. …”
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Machine learning using random forest to model heavy metals removal efficiency using a zeolite-embedded sheet in water by N.D. Takarina, N. Matsue, E. Johan, A. Adiwibowo, M.F.N.K. Rahmawati, S.A. Pramudyawardhani, T. Wukirsari

“…The random forest models were then validated using the root mean square error, mean square of residuals, percentage variable explained and graphs depicting out-of-bag error of a random forest.FINDINGS: The results show the heavy metal removal efficiency was 5.51-95.6 percent, 42.71-98.92 percent and 13.39-95.97 percent for copper, lead and zinc, respectively. …”
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Determination of Major Elements in Small-Weight Soil and Sediment Samples by X-Ray Fluorescence Spectrometry with Pressed-Powder Pellets by Hongkun ZHAO, Yaxuan LIU, Shengming MA, Yanfei ZHANG, Pengpeng ZHANG, Qiang LI, Zhenqing LI, Qishen CHEN, Yong LI, Xue GU, Hongqiang CHEN

“…Through comparative analysis, the results of the erence materials were all within the standard value range, and the absolute value of relative error (|RE|) was between 0 and 15.7%. …”
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Psychometric properties of the Chinese version of scales of knowledge, attitude, and practice of self-care for patients with arteriovenous fistula: a translation and verification s... by Chuang Li, Chuang Li, Youbei Lin, Jiaxin Cheng, Danfeng Xu, Danfeng Xu, Lan Zhang

“…Confirmatory factor analysis (CFA) demonstrated good model fit, with fit indices such as the chi-square value, Comparative Fit Index (CFI), Tucker–Lewis Index (TLI), and Root Mean Square Error of Approximation (RMSEA) meeting acceptable standards. …”
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TASA formulation for nonlinear radiative flow of Walter-B nanoliquid invoking microorganism and entropy generation by T. Hayat, Aqsa Razzaq, Sohail A. Khan, Aneeta Razaq

“…Dimensionless ordinary expressions are developed through suitable transformations. Optimal homotopy analysis method (OHAM) is invoked for convergence purposes. …”
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Research on Time Series Interpolation and Reconstruction of Multi-Source Remote Sensing AOD Product Data Using Machine Learning Methods by Huifang Wang, Min Wang, Pan Jiang, Fanshu Ma, Yanhu Gao, Xinchen Gu, Qingzu Luan

“…A comparison of five machine learning models showed that the random forest model performed optimally in AOD inversion, achieving a root mean square error (RMSE) of 0.11 and a coefficient of determination (R²) of 0.93. …”
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Parametric study and optimisation of supercritical extraction of Chlorella Vulgaris microalgae using Response surface methodology by Milap G. Nayak, Reena D. Gamit

“…Sovová model with high R2 and low residual error showed a close agreement between predicted and observed values of oil extraction yield. …”
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Soft detection model of corrosion leakage risk based on KNN and random forest algorithms by Yang YANG, Chengzhi LI, Xuan DU, Xiao YU, Shaohua DONG

“…Future research efforts should focus on enhancing data acquisition and analysis techniques, optimizing the model structure, and improving the model adaptability and accuracy across various application scenarios.…”
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Problem of Ametropia in Children in Russian Federation by El Yu. Markova, L. Yu. Bezmel’nitsyna, O. V. Kurganova, N. A. Pron’ko, L. V. Venediktova

“…According to the results of clinical and economic analysis, uncorrected refractive errors in children are characterized by a high level of economic and social burden of disease (the older the age of ametropia diagnosis caused increase of direct medical and non-medical costs). …”
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Advancing perovskite photovoltaic technology through machine learning‐driven automation by Jiyun Zhang, Jianchang Wu, Vincent M. Le Corre, Jens A. Hauch, Yicheng Zhao, Christoph J. Brabec

“…Traditional trial‐and‐error methods and manual analysis are inefficient and urgently need advanced strategies. …”
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Cloning, expression of carboxylesterase BioH and improvement of its hydrolysis activity by directed evolution by XU Hongna, YU Hongwei

“…The thermostability of the enzyme was the foundation in the enzyme application. The optimized concentration of Mn²⁺ in error-prone PCR was chosen as 0.1 mmol/L determined by agarose electrophoresis of the brightness of PCR product band. …”
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Complex Model for Hot Metal Temperature Prediction: Torpedo Car and Ladle Processes by Milan Durdán, Ján Terpák, Marek Laciak, Ján Kačur, Patrik Flegner, Gabriel Tréfa

“…This model, based on heat transfer by conduction, convection, radiation, heat accumulation, and chemical reactions, also allows for the monitoring of the hot metal temperature drop in the torpedo car and the ladle, the analysis of the influence of the linings in terms of heat accumulation, the investigation of the desulfurization process in the ladle, and the optimization torpedo and ladle selection in terms of the accumulated heat in the lining for their entry into the hot metal transport process. …”
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A Comparison of Traditional and Kernel Equating Methods by Selahattin Gelbal, Çiğdem Akın Arıkan

“…In the second step, the booklets were equated according tomethods. Lastly, the errors for each equating methods were calculated. …”
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A student academic performance prediction model based on the interval belief rule base by Wenkai Zhou, Yunsong Li, Jiaxing Li, Tianhao Zhang, Xiping Duan, Ning Ma, Yuhe Wang

“…Abstract Student performance prediction (SPP) constitutes one of the pivotal tasks in educational data analysis. Outcomes from the prediction enables educators to implement targeted interventions for students. …”
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Neural networking-based approach for examining heat transfer and bioconvection in Non-Newtonian fluid with chemical reaction over a stretching sheet by Sohaib Abdal, Talal Taha, Liaqat Ali, Rana Muhammad Zulqarnain, Se-Jin Yook

“…The dataset is split into 70 % train, 15 % test, and 15 % validate to maximize model precision and generalizability. Mean Squared Error (MSE) is utilized to measure precision, whereas regression analysis (R ≈ 1) verifies strong prediction accuracy. …”
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Fractal-Based Thermal Conductivity Prediction Modeling for Closed Mesoporous Polymer Gels by Haiyan Yu, Mingdong Li, Ning Guo, Anqi Chen, Haochun Zhang, Mu Du

“…Model predictions exhibit strong agreement with experimental results from various mesoporous polymer gels, achieving a prediction error of less than 11.2%. Furthermore, a detailed parametric analysis was conducted, elucidating the influences of porosity, cell size, temperature, refractive index, and extinction coefficient. …”
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Risk assessment of tunnel water inrush based on Delphi method and machine learning by Leizhi Dong, Qingsong Wang, Weiguo Zhang, Yongjun Zhang, Xiaoshuang Li, Fei Liu

“…To guarantee the accuracy of prediction results, only the correctly selected risk factors, validated by Grey Relational Analysis (GRA), are recognized as assessment indexes. …”
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The Future of Vineyard Irrigation: AI-Driven Insights from IoT Data by Simona Stojanova, Mojca Volk, Gregor Balkovec, Andrej Kos, Emilija Stojmenova Duh

“…The low value of the statistical analysis (<i>p</i>-value = 0.0009) of a paired <i>t</i>-test confirmed that the improvement is significant. …”
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