INTELLIGENT PREDICTION METHOD ON STABILITY OF THIN-WALL STEEL COMPONENT WITH NON CONTINUOUS CROSS SECTION by SONG YiMing, LV ZhiJun, CHEN Dong, WU YongMei

“…The predict results are compared with the test data,and the error is less than 2%,which is more accurate than the finite element analysis results. …”
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A Hybrid Machine Learning Approach: Analyzing Energy Potential and Designing Solar Fault Detection for an AIoT-Based Solar–Hydrogen System in a University Setting by Salaki Reynaldo Joshua, An Na Yeon, Sanguk Park, Kihyeon Kwon

“…Key elements of the methodology include data collection from solar energy production and fault detection systems, energy potential analysis using Transformer models, and fault identification in solar panels using CNN and ResNet-50 architectures. …”
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Mapping total suspended solids (TSS) and dissolved organic carbon (DOC) in complex coastal waters using deep learning enhanced remote sensing by S.L. Kesav Unnithan, Nagur Cherukuru, Timothy Ingleton, Eric Lehmann, Matt Paget, Yiqing Guo, Nathan Drayson, Gemma Kerrisk

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Modeling Airflow and Temperature in a Sealed Cold Storage System for Medicinal Plant Cultivation Using Computational Fluid Dynamics (CFD) by Sakkarin Wangkahart, Chaiyan Junsiri, Aphichat Srichat, Kittipong Laloon, Kaweepong Hongtong, Phaiboon Boupha, Somporn Katekaew, Sahassawas Poojeera

“…Model validation was performed using statistical analyses including coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE). The validation results showed strong agreement between simulated and experimental data, with R² values of 0.92 for temperature and 0.89 for airflow velocity. …”
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Nexus between environmental sustainability, energy intensity and food security: evidence from emerging economies by Md. Mahadi Hasan, A.T.M. Adnan

“…The analysis utilizes data spanning from 1980 to 2018 and encompasses 32 emerging economies. …”
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Determination of the Tail Unit Parameters of Ultralight Manned and Unmanned Helicopters at the Preliminary Design Stage by Vitaly Dudnik

“…The analysis shows that the results of parameter selection at the preliminary design stage have an error rate of between 11 and 30%. …”
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A workflow for sizing oligomeric biomolecules based on cryo single molecule localization microscopy. by Magdalena C Schneider, Roger Telschow, Gwenael Mercier, Montserrat López-Martinez, Otmar Scherzer, Gerhard J Schütz

“…We propose here a workflow for data analysis, which is based on SMLM performed at cryogenic temperatures. …”
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The Influence of Islamic Capital Market Capitalization and Labor Force on the Economic Growth of Indonesia by Deky Candra Saputra, Muhammad Ikhsan

“…The analysis method employed is quantitative analysis, and the Error Correction Model (ECM) is used for the analysis. …”
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POTRET BAHASA JAWA RAGAM KRAMA MASYARAKAT PESISIRAN KOTA SEMARANG by Fitri Windaryanti, M. Suryadi

“…Sociolinguistics approach with the the utilization of both quantitative and qualitative method is used in analysis data. Quantitative analysis is used to measure krama lexicon’s proficiency degree. …”
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THE EXPERIMENTAL DETERMINATION METHODOLOGY OF THE PARAMETERS IN THE EQUATION OF THE AIR TEMPERATURE DISTRIBUTION ON THE HFS HEIGHT by V. G. Gagarin, N. Yu. Plyushchenko, A. R. Kosarev

“…The calculations from the  obtained equation are compared with the experimental data. The comparative analysis shows an acceptable error between calculated and experimental data. …”
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Regression machine learning methods for isolation prediction and massive gain broadband MIMO antenna design for 28 GHz applications by Md.Ashraful Haque, Redwan A. Ananta, Jun-Jiat Tiang, Mouaaz Nahas, Md Afzalur Rahman, Narinderjit Singh Sawaran Singh

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Liver MRI proton density fat fraction inference from contrast enhanced CT images using deep learning: A proof-of-concept study. by Md Nasir, Yixi Xu, Kyle Hasenstab, Alekhya Yechoor, Rahul Dodhia, William B Weeks, Juan Lavista Ferres, Guilherme Moura Cunha

“…We evaluated performance using mean absolute error (MAE) and root mean squared error (RMSE), and mean errors (defined as the mean difference of results of comparator groups), with 95% confidence intervals (CIs). …”
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State of health prediction of lithium-ion batteries in charging chambers based on multi-modal deep learning by ZHAO Yinghua, CHEN Anbi, ZHANG Zengyu, LI Wenzhong, HAN Yu

“…Experimental results demonstrated that the proposed method achieved correlations exceeding 98%, with relatively low Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). …”
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Design, Construction, and Evaluation of a Spatial Depth Measurement System for Subsoiler in Sugarcane Fields by N. Loveimi, A. Azizi, A. Kaab, A. Neisi

“…Statistical analysis revealed that only indices related to minimum and maximum errors for interactions between factors were significant. …”
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A Multi-Spatial Scale Ocean Sound Speed Prediction Method Based on Deep Learning by Yu Liu, Benjun Ma, Zhiliang Qin, Cheng Wang, Chao Guo, Siyu Yang, Jixiang Zhao, Yimeng Cai, Mingzhe Li

“…Specifically, relative to the measured data, it achieved a root mean square error (RMSE) of approximately 0.57 m/s and a mean absolute error (MAE) of about 0.29 m/s. …”
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Statistics is not measurement: The inbuilt semantics of psychometric scales and language-based models obscures crucial epistemic differences by Jana Uher

“…These crucial epistemic differences are elaborated using Rosen's general model of measurement, involving the coherent modelling of the (1) objects of research, (2) data generation (encoding), (3) formal manipulation (e.g., statistical analysis) and (4) result interpretation regarding the objects studied (decoding). …”
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Determination of similar material proportions based on orthogonal experiments and neural network optimization in the goaf area by Shengdi Wang, Junzhi Chen, Yonggang Zhang, Xiaowei Qiao, Zhiping Zhang, Xin Wang

“…Furthermore, this study proposed a novel machine learning-based prediction model that utilizes a PSO-BP neural network to regress and predict experimental data. Compared with traditional BP neural network, the results show that the PSO-BP model has a higher prediction correlation coefficient R2 than the traditional BP model, while the root mean square error (RMSE) and mean absolute error (MAE) are lower than the traditional BP model, indicating that the PSO-BP model has better and more stable prediction performance.So PSO-BP neural network model can more accurately predict the optimal mixing ratio for similar materials. …”
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Diabetes: Non-Invasive Blood Glucose Monitoring Using Federated Learning with Biosensor Signals by Narmatha Chellamani, Saleh Ali Albelwi, Manimurugan Shanmuganathan, Palanisamy Amirthalingam, Anand Paul

“…Clarke error grid analysis (CEGA) confirmed the model’s clinical reliability, with 99.31% of predictions falling within clinically acceptable limits. …”
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Battery state of health estimation under fast charging via deep transfer learning by Jingyuan Zhao, Di Li, Yuqi Li, Dapai Shi, Jinrui Nan, Andrew F. Burke

“…With approximately 22,000 parameters, the model effectively estimates battery capacity by combining local feature extraction (convolutional neural networks [CNNs]) and global dependency analysis (self-attention). The model was validated on 222 lithium iron phosphate (LFP) batteries, encompassing 146,074 cycles, with limited data availability in a state of charge (SOC) range of 80%–97%. …”
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"Non-destructive and rapid determination of bound styrene content of styrene-butadiene rubber latex using near-infrared spectroscopy" by LI Yan, ZHONG Ming-li, ZHU Shi-yong, CUI Jia-min, ZHANG Jian-ping, CHEN Shi-long

“…A non-destructive and rapid determination of bound styrene content in styrene-butadiene rubber latex was studied using near-infrared spectroscopy diffuse transmission method combined with chemometrics, bound styrene content in styrene-butadiene rubber latex was determined by refractive index method, near-infrared spectral data of styrene-butadiene rubber latex were collected using Fourier transform near-infrared spectrometer, Kennard-Stone algorithm was used to divide the calibration set and validation set, partial least squares regression quantitative analysis model was established by combining the spectral preprocessing methods, such as multiple scattering correction method, second-order derivatives and Norris smoothing, etc, and the influence of screening spectral feature variables by interval partial least squares algorithm on the quantitative ana-lysis model was finally investigated. …”
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