Comparison of artificial intelligence approaches for estimating wind energy production: A real-world case study by Mohamed Bousla, Mohamed Belfkir, Ali Haddi, Youness El Mourabit, Badre Bossoufi

“…For performance robustness assessment, three distinct error metrics were employed to evaluate accuracy in weekly, monthly, and yearly forecasting scenarios, using the persistence model as a reference point. …”
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A Multihierarchy Flow Field Prediction Network for Multimodal Remote Sensing Image Registration by Wenqing Wang, Kunpeng Mu, Han Liu

“…At the same time, the photometric error loss is introduced to optimize the entire network from both the feature and original image levels. …”
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Impact of Instantaneous Parameter Sensitivity on Ensemble‐Based Parameter Estimation: Simulation With an Intermediate Coupled Model by Lige Cao, Guijun Han, Wei Li, Haowen Wu, Xiaobo Wu, Gongfu Zhou, Qingyu Zheng

“…Results show that the success of a parameter estimation strategy is closely tied to the significant IPS of the observed state to the parameter targeted for optimization, as it maintains a high signal‐to‐noise ratio in the error covariance between parameter and prior state, thereby enhancing parameter estimation. …”
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Prediction Model of Household Carbon Emission in Old Residential Areas in Drought and Cold Regions Based on Gene Expression Programming by Shiao Chen, Yaohui Gao, Zhaonian Dai, Wen Ren

“…., electricity usage and heating energy consumption) were selected using Pearson correlation analysis and the Random Forest (RF) algorithm. Subsequently, a hybrid prediction model was constructed, with its parameters optimized by minimizing the root mean square error (RMSE) as the fitness function. …”
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A Short-Term Load Forecasting Method Considering Multiple Factors Based on VAR and CEEMDAN-CNN-BILSTM by Bao Wang, Li Wang, Yanru Ma, Dengshan Hou, Wenwu Sun, Shenghu Li

“…Finally, the sine–cosine and Cauchy mutation sparrow search algorithm (SCSSA) is used to optimize the parameters of the combinative model to improve the forecasting accuracy. …”
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Super-Resolution Reconstruction of Motor Long-Wave Infrared Images Based on Improved USR-Net by Darong Zhu, Ziyan Sun, Fangbin Wang

“…The reconstruction performance is further optimized by combining perceptual loss and mean squared error loss. …”
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Predicting hydrocarbon reservoir quality in deepwater sedimentary systems using sequential deep learning techniques by Xiao Hu, Jun Xie, Xiwei Li, Junzheng Han, Zhengquan Zhao, Hamzeh Ghorbani

“…Three sequential deep learning models—Recurrent Neural Network and Gated Recurrent Unit—were developed and optimized using the Adam algorithm. The Adam-LSTM model outperformed the others, achieving a Root Mean Square Error of 0.009 and a correlation coefficient (R2) of 0.9995, indicating excellent predictive performance. …”
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A control strategy for shipboard stabilization platforms based on a fuzzy adaptive proportional–integral–derivative (PID) control architecture by H. Liu, Z. Zeng, X. Yang, Y. Zou, X. Liu

“…The kinematic model is established via analysis based on the virtual-work principle, complemented by Monte Carlo simulations for workspace characterization. …”
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Leveraging Spatial and Temporal Data to Predict Heavy Freight Vehicle Traffic Flow on Rural Road Network by Alireza Gholami, Seyedehsan Seyedabrishami

“…In addition to R-squared, model performance has been further evaluated using RMSE and MAE metrics, offering a more nuanced and reliable understanding of prediction accuracy across different error dimensions. Analysis based on SHAP values and sensitivity assessment demonstrates that particular times of day and traffic counter classifications exert the greatest influence on HFV traffic flow predictions. …”
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Empirical Modeling of the Carbonylation of Acetylene for the Synthesis of Succinic Anhydride Using Gas Ratio (CO/C₂H₂) by Collins Ubaka Mordi, Ipeghan Jonathan Otaraku, Ishioma Laurene Egun

“…This study focuses on the empirical modeling of the carbonylation process to optimize the gas ratio of carbon monoxide (CO) to acetylene (C₂H₂), a critical parameter influencing product yield. …”
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Data driven fuel consumption prediction model for green aviation using radial basis function neural network by Yuandi Zhao, Zhongyi Wang, Xiaohui Wang, Ye Song, Yuzhe Han

“…The error variances from ten-fold cross-validation were 0.31%, 0.15%, and 0.29%, respectively, confirming the robustness of the model. …”
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Implementation of MF block in CNN for advanced REB fault diagnosis by M. Pandiyan, Narendiranath Babu T.

“…Statistical significance tests and error bars validate the robustness of the model’s performance. …”
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Fusion of Satellite and UAV Imagery for Crop Monitoring by A. R. Allu, S. Mesapam

“…Crop monitoring is crucial for precision agriculture, providing insights for optimizing yield and managing resources effectively. …”
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Investigating Cumin Uprooting Dynamics: The Roles of Taproot Properties and Soil Resistance by Sheng Tai, Zhong Tang, Bin Li, Shiguo Wang, Xiaohu Guo

“…DEM simulation was employed for pull-out analysis. The static coefficient of friction between the cumin taproot and sandy loam soil was found to be approximately 0.766. …”
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Perceived Management of Acute Sports Injuries and Medical Conditions by Athletic Trainers and Physical Therapists by Alan Wallace, Matthew S. Briggs, James Onate, John DeWitt, Laurie Rinehart-Thompson

“…Kruskal-Wallis H-Tests and Spearman’s Rho Correlations were used for the analysis. Significance was set to p<0.003 after adjustment for family-wise error…”
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Mesoscale Modelling of the Mechanical Behavior of Metaconcretes by Antonio Martínez Raya, Gastón Sal-Anglada, María Pilar Ariza, Matías Braun

“…The model is validated against benchmark experiments, reproducing the 56% loss of compressive strength recorded for a 10.6% resonator volume fraction with an error of less than 1%. A parametric analysis covering resonator ratios from 1.5% to 31.8%, diameters from 16.8 mm to 37.4 mm, and coating thicknesses from 1.0 mm to 4.2 mm shows that (i) strength decays exponentially with volumetric content, approaching an asymptote at ≈20% of plain concrete strength; (ii) larger cores with thinner coatings minimize stiffness loss (<10%) while limiting strength reduction to 15–20%; and (iii) material properties of the resonator have a secondary influence (<6%). …”
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Enhancing Photovoltaic System Efficiency Through Fuzzy Logic-Based Maximum Power Point Tracking by N. A. Yusri, S. N. H. M. Tarmizi, M. I. Zakaria, S. F. M. Hussein, A. R. A. A. Emhemed

“…The MPPT process involves the analysis of system parameters, including current error and the rate of change of errors, which serve as the input to the fuzzy logic system. …”
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Molecular property prediction using pretrained-BERT and Bayesian active learning: a data-efficient approach to drug design by Muhammad Arslan Masood, Samuel Kaski, Tianyu Cui

“…Experiments on Tox21 and ClinTox datasets demonstrate that our approach achieves equivalent toxic compound identification with 50% fewer iterations compared to conventional active learning. Analysis reveals that pretrained BERT representations generate a structured embedding space enabling reliable uncertainty estimation despite limited labeled data, confirmed through Expected Calibration Error measurements. …”
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Detection and Prediction of Wind and Solar Photovoltaic Power Ramp Events Based on Data-Driven Methods: A Critical Review by Jie Zhang, Xinchun Zhu, Yigong Xie, Guo Chen, Shuangquan Liu

“…This review systematically examines recent advancements in the field, focusing on three principal areas: the definition and detection of ramp event characteristics, innovations in predictive model architectures, and strategies for precision optimization. Our analysis reveals that while detection algorithms for ramp events have matured and the overall predictive performance of power forecasting models has improved, existing approaches often struggle to capture localized ramp phenomena, resulting in persistent deviations. …”
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Noninvasive Glucose Measurements in Tissue Simulating Phantoms Using a Solid-State Near-Infrared Sensor by Ariel B. Kauffman, Ruben Shakya, Shuai Yu, Mark A. Arnold

“…Results for the laser prototype data included a standard error of cross validation (SECV) of 7.82 mg/dL for an optimized PLS model with 10 factors over a spectral range of 1401–2238 nm. …”
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