Quantifying optimal inner limiting membrane peeling in macular hole surgery: a machine learning framework for predictive modeling and schematic visualization by Xiang Zhang, Hongjie Ma, Song Lin, Ledong Zhao, Lu Chen, Zetong Nie, Zhaoxiong Wang, Chang Liu, Xiaorong Li, Wenbo Li, Bojie Hu

“…Abstract Purpose Internal limiting membrane (ILM) peeling in macular hole (MH) surgery is critical but challenging, and current practices lack standardized tools for quantifying and visualizing optimal peeling dimensions.This study aimed to develop a machine learning framework to recommend surgeon-specific ILM peeling radius during macular hole surgery, integrating predictive modeling with schematic visualization to guide operative planning. …”
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Quaternion generative adversarial -driven Soc estimation using Tyrannosaurus optimizer for improving hybrid electric vehicles renewably powered energy management by M. Sivaramkrishnan, Jaganathan Subramani, Mohammad Mukhtar Alam, Liew Tze Hui

“…The suggested approach shows excellent accuracy with few errors for various drive cycles and temperatures: for US06, the RMSE stabilizes at about 0.05%, the MAE drops to 0.1%, and the MSE reaches 0.0025%.…”
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Proactive synergistic control of PM2.5 and ozone through urban planning: longitudinal data analysis of 274 Chinese cities from 2005 to 2020 by Sha Li, Bin Zou, Ning Liu, Chenhao Xue, Shenxin Li, Yulong Wang, Yong Xu

“…Subsequently, it designs regulatory pathways incorporating regulatable UFI thresholds and air quality optimization targets. Validation across 274 Chinese cities (2005–2020) showed the model’s robust predictive performance, achieving coefficients of determination (R2) of 0.88 and 0.89 for PM2.5 and O3, and root mean squared errors (RMSE) of 5.09 μg/m3 and 4.71 μg/m3 for PM2.5 and O3, respectively. …”
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Enhancing Compressive Strength Prediction in Recycled Aggregate Concrete through Robust Hybrid Machine Learning Approaches by Samuel Keown, Dylan O’Dwyer

“…This investigation explores the integration of LSSVR with two innovative optimizers, namely the Giant Trevally Optimizer (GTO) and the Dingo Optimization Algorithm (DOA). …”
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A Novel Back Propagation Neural Network Based on the Harris Hawks Optimization Algorithm for the Remaining Useful Life Prediction of Lithium-Ion Batteries by Yuyang Zhou, Zijian Shao, Huanhuan Li, Jing Chen, Haohan Sun, Yaping Wang, Nan Wang, Lei Pei, Zhen Wang, Houzhong Zhang, Chaochun Yuan

“…Subsequently, two key health factors are selected as input features for the model, including the constant-current charging isovoltage rise time and constant-current discharging isovoltage drop time. …”
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Machine learning enhanced ultra-high vacuum system for predicting field emission performance in graphene reinforced aluminium based metal matrix composites by Sunil Kumar Pradhan, Subhayu Kabiraj, Shivin Kumar Gupta, Abhishek Singh, Padmakar G. Chavan, Shubham S. Patil, Trilok Nath Pandey

“…Model evaluation was conducted based on R²(R-squared), RMSE (Root Mean Squared Error), and Adjusted R² scores. …”
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Construction and application of a digital twin model for multi-objectiveoptimization of intelligent tape conveyor system by Wei CHEN, Jingzhao LI, Qing SHI, Jichao LIU, Huashun LI

“…The Improved Whale Optimization Algorithm (IWOA) is employed to optimize parameters in this blueprint, facilitating real-time scheduling by rapidly converging to the global optimum. …”
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Analyzing Tractor Productivity and Efficiency Evolution: A Methodological and Parametric Assessment of the Impact of Variations in Propulsion System Design by Ivan Herranz-Matey

“…The analysis culminates in the creation of a robust, user-friendly parametric model (R² = 0.9337, RMSE = 1.0265), designed to assist stakeholders in making informed decisions regarding tractor replacement or upgrading. …”
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Estimating Chlorophyll-a Concentrations in Optically Shallow Waters Using Gaofen-1 Wide-Field-of-View (GF-1 WFV) Datasets from Lake Taihu, China by Fuli Yan, Yuzhuo Li, Xiangtao Fan, Hongdeng Jian, Yun Li

“…Due to the bottom effect of submerged aquatic plants in optically shallow waters, currently available phytoplankton chlorophyll-a retrieval algorithms tend to overestimate chlorophyll-a concentrations in the eastern part of Lake Taihu. …”
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Integrating Multilayer Perceptron and Support Vector Regression for Enhanced State of Health Estimation in Lithium-Ion Batteries by Sadiqa Jafari, Jisoo Kim, Wonil Choi, Yung-Cheol Byun

“…Our approach includes meticulous data preparation, which includes analyzing crucial operating elements such as voltage, current, and temperature. We utilized Support Vector Regression (SVR) and Multilayer Perceptron (MLP) models, which were fine-tuned using hyperparameter optimization. …”
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Prediction of Total Organic Carbon Content in Shale Based on PCA-PSO-XGBoost by Yingjie Meng, Chengwu Xu, Tingting Li, Tianyong Liu, Lu Tang, Jinyou Zhang

“…Total organic carbon (TOC) content is an important parameter for evaluating the abundance of organic matter in, and the hydrocarbon production capacity, of shale. Currently, no prediction method is applicable to all geological conditions, so exploring an efficient and accurate prediction method suitable for the study area is of great significance. …”
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Assimilating Satellite-Based Biophysical Variables Data into AquaCrop Model for Silage Maize Yield Estimation Using Water Cycle Algorithm by Elahe Akbari, Ali Darvishi Boloorani, Jochem Verrelst, Stefano Pignatti

“…Accurate crop yield estimation is critical to successful agricultural operations. Current crop growth models often overlook the spatial and geographic components of the lands, leading to suboptimal yield estimates. …”
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Accurate Rotor Temperature Prediction of Permanent Magnet Synchronous Motor in Electric Vehicles Using a Hybrid RIME-XGBoost Model by Jianzhao Shan, Zhongyuan Che, Fengbin Liu

“…RIME-XGBoost utilizes easily monitored dynamic parameters such as motor speed, torque, and currents and voltages in the d-q coordinate system as input features. …”
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Concrete Dam Deformation Prediction Model Based on Attention Mechanism and Deep Learning by ZHANG Hongrui, CAO Xin, JIANG Chao, ZU Anjun, XU Mingxiang

“…Key hyperparameters, which significantly influence model performance, were automatically optimized using SSA to minimize subjective manual pre-setting and avoid the randomness of artificial parameter configuration. …”
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Forecasting and Comparative Application of PV System Electricity Generation for Sprinkler Irrigation Machines Based on Multiple Models by Bohan Li, Kenan Liu, Yaohui Cai, Wei Sun, Quan Feng

“…Currently, photovoltaic (PV) resources have been widely applied in the agricultural sector. …”
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Sensorless real-time solar irradiance prediction in grid-connected PV systems using PSO-MPPT and IoT-enabled monitoring by Ali Zaki Mohammed Nafa, Adel A. Obed, Ahmed J. Abid, Salam J. Yaqoob, Mohit Bajaj, Mohammad Shabaz

“…The approach leverages the maximum power point current ( $$\:{\text{I}}_{\text{mpp}}$$ ) and voltage ( $$\:{\text{V}}_{\text{mpp}}$$ ) measured directly from a PV module to predict irradiance, utilizing a Particle Swarm Optimization (PSO)-based Maximum Power Point Tracking (MPPT) algorithm to ensure accurate tracking of power output across varying irradiance levels. …”
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An application of the GWO-ELM hybrid model to the accurate 2 prediction of solar radiation for the purpose of sustainable energy 3 integration by Fangyuan Li

“…Performance indicators for the proposed GWO-ELM model include RMSE of 63.17, MAE of 46.68, MSE of 3990.80, and RSE of 89.39. …”
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Forest canopy closure estimation in mountainous southwest China using multi-source remote sensing data by Wenwu Zhou, Wenwu Zhou, Qingtai Shu, Cuifen Xia, Li Xu, Qin Xiang, Lianjin Fu, Zhengdao Yang, Shuwei Wang

“…The research results showed that (1) among the 50 extracted ATLAS LiDAR feature indices, the best footprint-scale modeling factors are Landsat_perc, h_dif_canopy, asr, h_min_canopy, toc_roughness, and n_touc_photons after random forest (RF) feature variable optimization; (2) among the BO-RFR, BO-KNN, and BO-GBRT models developed at the footprint scale, the FCC results estimated by the BO-GBRT model were the best (R2 = 0.65, RMSE = 0.10, RS = 0.079, and P = 79.2%), which was used as the FCC estimation model for 74,808 footprints in the study area; (3) taking the FCC value of ATLAS footprint scale in forest land as the training sample data of the regional-scale GWR model, the model accuracy was R2 = 0.70, RMSE = 0.06, and P = 88.27%; and (4) the R² between the FCC estimates from regional-scale remote sensing and the measured values is 0.70, with a correlation coefficient of 0.784, indicating strong agreement. …”
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Employment of a Radial Basis Function Model for Predicting the Heating Load of Construction by Yuxuan Dai

“…Nowadays, the main focus of current research and practice is to prioritize energy-efficient building management. …”
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Comparison of Three Temperature and Emissivity Separation Algorithms for Graybodies with Low Spectral Contrast: A Case Study on Water Bodies by Min Xiao, Shugui Zhou, Jie Cheng

“…The temperature and emissivity separation (TES) algorithm is currently adopted to retrieve the land surface temperature (LST) and emissivity (LSE) from Moderate Resolution Imaging Spectroradiometer (MODIS) images (i.e., the MOD/MYD21 product). …”
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