Mean limiting pressure factors determination in contiguous pile walls using RAFELA and nonlinear regression models in spatially random soil by Divesh Ranjan Kumar, Sittha Kaorapapong, Warit Wipulanusat, Suraparb Keawsawasvong

“…The models were evaluated using several statistical performance parameters, scatter plots, residual error curves, and eight statistical performance metrics to ensure predictive accuracy and reliability. …”
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International Natural Uranium Price Prediction Based on TF-CNN-BiLSTM Model by YANG Jingzhe, XUE Xiaogang

“…However, the model’s performance could be further enhanced by incorporating additional relevant features such as geopolitical indicators, economic indices, and policy variables. …”

Evidential Analysis: An Alternative to Hypothesis Testing in Normal Linear Models by Brian Dennis, Mark L. Taper, José M. Ponciano

“…Statistical hypothesis testing, as formalized by 20th century statisticians and taught in college statistics courses, has been a cornerstone of 100 years of scientific progress. …”
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Accuracy Characteristics of the Parametric Burg Method for Spatial Signal Processing in a Nonuniform Array Antenna by V. M. Kutuzov, M. A. Ovchinnikov, E. A. Vinogradov

“…Under low signal-to-noise ratios (SNR), this noise leads to angular coordinate measuring errors thus worsening the statistical accuracy characteristics (ACs) of the signal. …”
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A novel deep learning framework with artificial protozoa optimization-based adaptive environmental response for wind power prediction by Sangkeum Lee, Mohammad H. Almomani, Saleh Ali Alomari, Kashif Saleem, Aseel Smerat, Vaclav Snasel, Amir H. Gandomi, Laith Abualigah

“…Results demonstrate that IAPO-LSTM achieved the lowest forecasting errors across all datasets, with Mean Absolute Error (MAE) as low as 2.78, Root Mean Square Error (RMSE) of 4.50, and Theil’s Inequality Coefficient (TIC) of 0.0292 on the ERCOT dataset. …”
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A Novel Fractional Order Multivariate Partial Grey Model and Its Application in Natural Gas Production by Hui Li, Huiming Duan, Hongli Chen

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DASNet a dual branch multi level attention sheep counting network by Yini Chen, Ronghua Gao, Qifeng Li, Hongtao Zhao, Rong Wang, Luyu Ding, Xuwen Li

“…Accurate population statistics help optimize livestock management and sustain grassland ecosystems. …”
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Tool Wear Estimation in the Milling Process Using Backpropagation-Based Machine Learning Algorithm by Giovanni Oliveira de Sousa, Pedro Oliveira Conceição Júnior, Ivan Nunes da Silva, Dennis Brandão, Fábio Romano Lofrano Dotto

“…This work focuses on an application of tool wear estimation using a simple backpropagation neural network in a milling dataset. Statistical techniques, i.e., the mean, variance, skewness, and kurtosis, were used as features that were extracted from indirect measurements from vibration and acoustic emission sensors’ data in a real milling testbench dataset containing multiple experiments with sensor data and a direct measure of the flank wear (VB) in most instances. …”
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A Black Box Doubly Fed Wind Turbine Electromechanical Transient Structured Model Fault Ride-Through Control Identification Method Based on Measured Data by Xu Zhang, Shenbing Ma, Jun Ye, Lintao Gao, Hui Huang, Qiman Xie, Liming Bo, Qun Wang

“…Firstly, according to the domestic and international new energy turbine fault ride-through standards, the fault ride-through segmentation control characteristics are summarized, and a general structured model for fault ride-through segmentation control of doubly fed wind turbines is constructed; Secondly, based on the measured hardware-in-the-loop data of the doubly fed wind turbine black box controller, the method of data segmentation preprocessing and structured model identification of the doubly fed wind turbine is proposed by utilizing statistical modal features and genetic Newton’s algorithm, and a set of generalized software simulation platforms for parameter identification is developed by combining Matlab and BPA; lastly, using the measured data of the doubly fed wind turbine in the black box and the software platform, the validity and accuracy of the proposed parameter identification method and software are tested in the simulation. …”
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Kinship analyses in forensic genetics: when complex hypotheses meet (very) complex genotypes by Marisa Faustino, Leonor Gusmão, António Amorim, Daniel Kling, Nádia Pinto

“…Theoretical and statistical frameworks were already established assuming euploid individuals, failing to address those with an X chromosome aneuploidy [1], such as those with Trisomy X (47, XXX), Klinefelter (47, XXY) and Turner (45, X0), which are the most common. …”
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Gait stability prediction through synthetic time-series and vision-based data by Mauricio C. Cordeiro, Ciaran O. Cathain, Ciaran O. Cathain, Ciaran O. Cathain, Vitor B. Nascimento, Thiago B. Rodrigues

“…and (3) what specific biomechanical features contribute most significantly to the MoS predictions in older adults? …”
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Comparison and Evaluation of Rain Gauge, CMORPH, TRMM PR and GPM DPR KuPR Precipitation Products over South China by Rui Wang, Huiping Li, Hao Huang, Liangliang Li

“…Meanwhile, CMORPH (1.5–6.0 mm/h) shows larger deviations from rain gauge than TRMM and GPM, and the bias progressively increases as rain rates rise, as indicated by root mean square error results. Several statistical metrics suggest that although the missing detection rates of TRMM and GPM are higher than those of CMORPH (probability of detection 10–60%), their false detection rates are spatially lower (false alert ratio 10–30%) in Middle-East China. …”
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<i>H</i>_∞ Preview Tracking Control of Time-Delay Discrete Systems and Its Application in Nuclear Reactor Problems by Fucheng Liao, Hao Xie, Xianchun Meng, Jiang Wu, Yucheng Wei, Jiamei Deng

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ENHANCING WEIGHTED FUZZY TIME SERIES FORECASTING THROUGH PARTICLE SWARM OPTIMIZATION by Armando Jacquis Federal Zamelina, Suci Astutik, Rahma Fitriani, Adji Achmad Rinaldo Fernandes, Lucius Ramifidisoa

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IoT-Enhanced Smart Parking Management With IncepDenseMobileNet for Improved Classification by Xiaoxia Zheng, Wenxi Feng, Ning Wang, Huhemandula

“…Advanced preprocessing techniques, such as Temporal Variability Adjustment and Harmonic Noise Compensation, enhanced data quality, while Proportional Adaptive Balancing and Augmentation (PABA) addressed class imbalance. The Hybrid Adaptive Feature Selector (HAFS) enhanced critical attributes via statistical and genetic diversity techniques. …”
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A gradient-boosted tree framework to model the ice thickness of the world's glaciers (IceBoost v1.1) by N. Maffezzoli, N. Maffezzoli, N. Maffezzoli, E. Rignot, E. Rignot, C. Barbante, C. Barbante, T. Petersen, S. Vascon

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Machine learning-based predictive analysis of energy efficiency factors necessary for the HIFU treatment of adenomyosis by Ziyan Liu, Ziyi Liu, Yuan Wang, Xiyao Wan, Xiaohua Huang

“…EEF values were calculated based on T2WI fat suppression (T2WI-FS) sequences, and radiomics features were extracted. Predictive features were selected using minimum redundancy maximum relevance (MRMR) and least absolute shrinkage and selection operator (LASSO) methods, and two joint—based on decision tree and random forest algorithms—models were developed for EEF prediction.ResultsThe decision tree model achieved a mean absolute error (MAE) of 8.095 on the test set, while the random forest model exhibited an MAE of 8.231. …”
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PPLIO: Plane-to-Plane LiDAR-Inertial Odometry With Multi-View Constraint in Real-Time by Hanyeol Lee, Jae Hyung Jung, Won Young Chung, Suyong Lee, Chan Gook Park

“…Since these methods only use distance as a parameter to associate feature points, they are prone to a mismatch of features in the presence of initial errors. …”
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Initialization improvement and clustering quality evaluation of K-means algorithm by HE Xuansen, HE Fan, YU Hailan

“…The simulation results show that the average λ test statistic of other algorithms is 2.72 times that of this scheme, and the improved clustering error is reduced by 6.04%.…”
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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

“…Feature importance analysis using SHAP values, together with non-parametric statistical testing, identified TVOCs, humidity, and PM_2.5 as the most influential predictors of energy use. …”
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