Predicting the Evolution of Capacity Degradation Histograms of Rechargeable Batteries Under Dynamic Loads via Latent Gaussian Processes by Daocan Wang, Xinggang Li, Jiahuan Lu

“…The model integrates Principal Component Analysis with GP regression to learn temporal degradation patterns from partial early-cycle data of a target cell, using a fully degraded reference cell. …”
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Simulation study on the dynamic similarity of oil-liquid spring recoil mechanism by Jun-Yu Shan, Zhong-Hua Du, Shuai Yue, Ming Hu

“…Further comparison of the similarity coefficient and normalized root mean square error values demonstrates that the similar and test model data exhibit good fitting and similar trends. …”
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Difference Equation Model-Based PM2.5 Prediction considering the Spatiotemporal Propagation: A Case Study of Bohai Rim Region, China by Ceyu Lei, Xiaoling Han, Chenghua Gao

“…The mean absolute error (MAE) of the forecast data for each urban agglomeration is within 7 units μg/m3. …”
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MAP OF PLOWED SOILS OF RUSSIA by I. Yu. Savin, V. S. Stolbovoy, S. A. Avetyan, E. A. Shishkonakova

“…The paper presents the results of the of the country's plowed area assessment on the basis of the Unified State Register of Soil Resources of Russia, a digital land cover map created on the basis of MODIS satellite data, and study of plowed land areas according to the data of the Internet service Google Earth. …”
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Development and psychometric evaluation of the artificial intelligence attitude scale for nurses by Tuğba Öztürk Yıldırım, Mesut Karaman

“…The item pool was developed through a comprehensive literature review. Data analysis included descriptive statistics, item analysis, and exploratory and confirmatory factor analyses, as well as assessments of convergent and divergent validity, correlation analysis, internal consistency reliability, and test-retest reliability. …”
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Difficulties of Enterobacteriaceae genome annotation in deciphering gastrointestinal microbiome datasets obtained by 16S rRNA gene amplicon sequencing by Elizaveta S. Klimenko, Natalya L. Belkova, Anna V. Pogodina, Lubov V. Rychkova, Marina A. Darenskaya

“…However, its use is complicated by an error rate of 10–17% when annotating 16S rRNA gene sequences. …”
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DrFARM: identification of pleiotropic genetic variants in genome-wide association studies by Lap Sum Chan, Gen Li, Eric B. Fauman, Xianyong Yin, Markku Laakso, Michael Boehnke, Peter X. K. Song

“…Through extensive simulations, we show that DrFARM appropriately controls overall FDR. Applying DrFARM to data on 1031 metabolites measured on 6135 men from the Metabolic Syndrome in Men (METSIM) study, we identify five first-time reported putative causal genes, none of which had been implicated in any prior metabolite GWAS (including the prior METSIM analysis).…”
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Adaptive neuro-fuzzy inference system optimization of natural rubber latex modified concrete’s mechanical Properties by Efiok Etim Nyah, David Ogbonna Onwuka, Joan Ijeoma Arimanwa, George Uwadiegwu Alaneme, G. Nakkeeran, Ulari Sylvia Onwuka, Chinenye Elizabeth Okere

“…The ANFIS model exhibited strong predictive performance, with a root mean square error (RMSE) of 1.5434, mean absolute percentage error (MAPE) of 2.89%, and R2 of 0.9795 for the modulus of elasticity. …”
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Bank Syariah Indonesia Share Price Prediction Using Fuzzy Time Series Model Lee Method by Luqmanul Hakiem Ajuna, Hendra H. Dukalang, Muhammad Ardi

“…This research uses the highest price value of Bank Syariah Indonesia shares, and the data analysis technique used in this study is descriptive statistical analysis Based on the results of research conducted obtained the results of the highest price prediction of Bank Syariah Indonesia daily shares for the next period is as of July 21, 2021 is $2,492.67 per share, and the fuzzy time series error rate using MAPE is 2.28263%.   …”
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Accuracy and precision in DM intake prediction models for lactating dairy cows by N. Mehaba, S. Schrade, L. Eggerschwiler, F. Dohme-Meier, P. Schlegel

“…The models were assessed for RMSE of prediction, including its decomposition into error of central tendency (ECT), error of regression, and error due to disturbance. …”
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Modeling and Control of a 3DOF Robot Manipulator Using Artificial Fuzzy-Immune FOPID Controller by Worod Adris Shutnan, Nora Ahmed Mohammed, Faris Asaad Abdulmunem, Ali Hamzah Najim, Mustafa Yahya Hassan, Naglaa F. Soliman, Abeer D. Algarni

“…The controllers’ efficacy was verified through quantitative analysis that employed performance indices, including mean square error (MSE). …”
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Day-ahead photovoltaic power generation forecasting with the HWGC-WPD-LSTM hybrid model assisted by wavelet packet decomposition and improved similar day method by Ruxue Bai, Jinsong Li, Jinsong Liu, Yuetao Shi, Suoying He, Wei Wei

“…This comparison is based on four performance metrics: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), normalized Root Mean Square Error (NRMSE), and Mean Absolute Deviation (MAD). …”
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A Novel Rubber Composite Sleeper-Deformation-Prediction Model Based on Response Surface Method (RSM) and Machine Learning (ML) Techniques by Abdulmumin Ahmed Shuaibu, Zhiping Zeng, Ibrahim Hayatu Hassan, Wang Weidong, Hassan Suleiman Otuoze, Suleiman Abdulhakeem, Bushrah Baba Abdulrahman

“…Model performance was evaluated using the correlation coefficient (R2), mean square error (MSE), root means square error (RMSE), and mean absolute error (MAE). …”
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Interval-based Uncertainty Bounding for Terrestrial Laser Scanning Observations by R. Naeimaei, S. Schön

“…Unlike the limited, pre-selected, and point-based measurements used in traditional approaches, TLS samples the environment with millions of points without the need to signal the points. Point clouds data from TLS are influenced by remaining systematic errors as well as random noise. …”
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The Prediction of Civil Building Energy Consumption Using a Hybrid Model Combining Wavelet Transform with SVR and ELM: A Case Study of Jiangsu Province by Xiangxu Chen, Jinjin Mu, Zihan Shang, Xinnan Gao

“…The wavelet transform technique is then applied to decompose the energy consumption data into a trend component and a fluctuation component. …”
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Selection of Prediction Method of Basic Statistical Work Parameters of N.V. Sklifosovsky Research Institute for Emergency Medicine of the Moscow Healthcare Department by B. L. Kurilin, V. Y. Kisselevskaya-Babinina, N. A. Karasyov, I. V. Kisselevskaya-Babinina, E. V. Kislukhkina, V. A. Vasilyev

“…Aim of study Determining the most optimal method for predicting the work of a medical institution, based on identification of the main trends in the time series when constructing a model of the dependence of parameters or determining the behavior of data as a stochastic series (i.e. modeling random processes and random events with some random error).Material and methods To predict the main statistical indicators of N.V. …”
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Spectrophotometric methods for determination of naringin, amlodipine, and nifedipine using chemometric techniques by Vishala Rani Baraily, Jithendar Reddy Mandhadi, Bhupendra Shrestha

“…Conclusion: The research demonstrated OPLS (Orthogonal Partial Least Squares) as a powerful solution for resolving overlapping spectral data, providing high-precision drug analysis with minimal interference. …”
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From Patterns to Predictions: Spatiotemporal Mobile Traffic Forecasting Using AutoML, TimeGPT and Traditional Models by Hassan Ayaz, Kashif Sultan, Muhammad Sheraz, Teong Chee Chuah

“…Model accuracy is assessed using standard evaluation metrics, including root mean square error (RMSE), mean absolute error (MAE) and the coefficient of determination (R²). …”
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Machine learning models for predicting tibial intramedullary nail length by Sercan Capkin, Ali Ihsan Kilic, Hakan Cici, Mehmet Akdemir, Mert Kahraman Marasli

“…Conventional techniques frequently depend on data obtained intraoperatively, which may prolong surgical time and elevate radiation exposure. …”
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Estimation of Static Lung Volumes and Capacities From Spirometry Using Machine Learning: Algorithm Development and Validation by Scott A Helgeson, Zachary S Quicksall, Patrick W Johnson, Kaiser G Lim, Rickey E Carter, Augustine S Lee

“…ResultsA total of 121,498 pulmonary function tests were used in this study, with 85,017 allotted for exploratory data analysis and model development (ie, training dataset) and 36,481 tests reserved for model evaluation (ie, testing dataset). …”
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