Machine Learning Approach to Investigating Macrophage Polarization on Various Titanium Surface Characteristics by Changzhong Chen, Zhenhuan Xie, Songyu Yang, Haitong Wu, Zhisheng Bi, Qing Zhang, Yin Xiao

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Machine learning prediction of interfacial bond strength of FRP bars with different surface characteristics to concrete by Lingyu Tian, Luchen Wang, Guijun Xian

“…The bond strength of FRP bars to concrete depends on the surface characteristics considerably. This study used machine learning (ML) techniques to explore the influence of bar surface types on the bond properties quantitatively. …”
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Rail surface identification and adhesion control based on dynamic adhesion characteristics by Wen Liu, Hongfeng Qi, Jingchun Huang, Yiyuan Chen, Sheng He, Haoxiang Feng

“…Therefore, to achieve stable tracking of the optimal adhesion state while adopting the feedback dynamic adhesion characteristic model, this study utilizes a machine learning algorithm to perform classification training on discrete points of the simulated adhesion characteristic model under various rail conditions. …”
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Preliminary Study on Near-Surface Air Temperature Lapse Rate Estimation and Its Spatiotemporal Distribution Characteristics in Beijing–Tianjin–Hebei Mountainous Region by Qichen Lv, Mingming Sui, Shanyou Zhu, Guixin Zhang, Yuxin Li

“…Based on reconstructed FY-4A AGRI LST data, this study downscales the 4 km resolution data to a 1 km resolution using machine learning. It then estimates the spatial distribution of near-surface air temperature (SAT) and normalized near-surface air temperature (nSAT) by integrating station observations. …”
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Cross-Domain Adversarial Learning for Sea Surface Temperature Super-Resolution by Wenhui Li, Jingyi Wang, Dan Song, Zhengya Sun, Zhiqiang Wei, An-An Liu

“…High-resolution sea surface temperature (SST) data can provide more accurate information on ocean conditions, which plays a significant role for environmental monitoring and climate research. …”
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Predictive modeling for mechanical characteristics of ultra high-performance concrete blended with eggshell powder and nano silica utilizing traditional technique and machine learn... by Yi Zhang, Qizhi Zhang, Ali H. AlAteah, Ahmed Essam, Sahar A. Mostafa

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Remote sensing with machine learning for multi-decadal surface water monitoring in Ethiopia by Mathias Tesfaye, Lutz Breuer

“…We identify the locations of this variability by analyzing the frequency of water occurrence over time and find that 84–94% are permanent water bodies, with the remaining water surface area changing over time. Mann–Kendall trend analysis does not confirm a general pattern over time for the four sites, suggesting that local site characteristics, water management and anthropogenic impacts are superimposed on the likely effects of climate change. …”
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Cross-dataset evaluation of deep learning models for crack classification in structural surfaces by Rashid Taha, Mokji Musa Mohd, Rasheed Mohammed

“…ResNet50 had managed to hold its own across the orchards of domains but was still a little troubled with the variability of the surface and noise, whereas LSTM became less useful as it struggled with the extraction of spatial characteristics. …”
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An Interpretable Machine Learning Framework for Unraveling the Dynamics of Surface Soil Moisture Drivers by Zahir Nikraftar, Esmaeel Parizi, Mohsen Saber, Mahboubeh Boueshagh, Mortaza Tavakoli, Abazar Esmaeili Mahmoudabadi, Mohammad Hassan Ekradi, Rendani Mbuvha, Seiyed Mossa Hosseini

“…Understanding the impacts of the spatial non-stationarity of environmental factors on surface soil moisture (SSM) in different seasons is crucial for effective environmental management. …”
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Ensemble Learning-Based Metamodel for Enhanced Surface Roughness Prediction in Polymeric Machining by Elango Natarajan, Manickam Ramasamy, Sangeetha Elango, Karthikeyan Mohanraj, Chun Kit Ang, Ali Khalfallah

“…This paper proposes and demonstrates a domain-adapted ensemble machine learning approach for enhanced prediction of surface roughness (Ra) during the machining of polymeric materials. …”
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Using deep learning to capture gravel soil microstructure and hydraulic characteristics by Bin Zhu, Yu-Fei Xie, Xiang-Gang Hu, Dai-Rong Su

“…The results demonstrated a high consistency between the reconstructed model of gravel soil realizations and the original samples in terms of porosity, two-point correlation function, linear path function, specific surface, and Euler characteristics number. Furthermore, through the evaluation of permeability, it was shown that the reconstructed realizations effectively captured and represented the actual soil prototype. …”
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Forecasting Surface Velocity Fields Associated With Laboratory Seismic Cycles Using Deep Learning by G. Mastella, F. Corbi, J. Bedford, F. Funiciello, M. Rosenau

“…Abstract It has been recently demonstrated that Machine Learning (ML) can predict laboratory earthquakes. Here we propose a prediction framework that allows forecasting future surface velocity fields from past ones for analog experiments of megathrust seismic cycles. …”
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Enhancing Defect Detection on Surfaces Using Transfer Learning and Acoustic Non-Destructive Testing by Michele Lo Giudice, Francesca Mariani, Giosuè Caliano, Alessandro Salvini

“…A convolutional neural network (CNN), initially trained on this mock-up, was then fine-tuned via transfer learning on a second test object with distinct geometry and material characteristics. …”
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Identification of Gingival Inflammation Surface Image Features Using Intraoral Scanning and Deep Learning by Wei Li, Linlin Li, Wenchong Xu, Yuting Guo, Min Xu, Shengyuan Huang, Dong Dai, Chang Lu, Shuai Li, Jiang Lin

“…Conclusion: The study has successfully developed an automatic identification method for surface characteristics of gingival inflammation based on deep learning and IOS technology, providing a standardised and automated auxiliary tool for clinical gingival inflammation examination with high accuracy and significant correlation with clinical indicators. …”
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Research on Air Temperature Inversion Method Based on Land Surface Temperature of Different Land Surface Cover by Rui Fang, Xiaofang Shan, Qinli Deng

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Deep learning-based reconstruction of monthly Antarctic surface air temperatures from 1979 to 2023 by Ziqi Ma, Jianbin Huang, Xiangdong Zhang, Yong Luo, Tingfeng Dou, Minghu Ding

“…However, significant discrepancies exist between the available Antarctic gridded temperature datasets, particularly regarding the spatial distribution characteristics of long-term temperature trends. In this paper, we develop a new, regularly updated, spatio-temporally complete Antarctic monthly SAT dataset from 1979 onwards, with a spatial resolution of 1° x 1° in latitude and longitude, from multiple sources of in situ observations using deep learning method. …”
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A Study on Path Planning for Curved Surface UV Printing Robots Based on Reinforcement Learning by Jie Liu, Xianxin Lin, Chengqiang Huang, Zelong Cai, Zhenyong Liu, Minsheng Chen, Zhicong Li

“…To address this challenge, this paper proposes a reinforcement-learning-based path planning method. First, an ideal main path is defined based on the nozzle characteristics, and then a robot motion accuracy model is established and transformed into a Markov Decision Process (MDP) to improve path accuracy and smoothness. …”
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Exploring the relationship between learning approaches and problem-based learning: insights from a longitudinal study in medical students by D Avraam, I Televantou, AP Albert, AW Hitchings, SA Nicolaou, A Papageorgiou, P McCrorie, P Nicolaou

“…Learners adopted less favourable learning approaches over the year, with increasing reliance on surface learning and less deep motivation. …”
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Quantitative characterization of fracture surfaces of granite specimens under triaxial extension using SEM and deep learning by Zida Liu, Diyuan Li, Zong-Xian Zhang, Chenxi Zhang, Quanqi Zhu

“…By combining scanning electron microscope and deep learning, the shear morphology on fracture surfaces of granite under triaxial extension with different confining pressures was quantificationally identified. …”
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Terrain Analysis According to Multiscale Surface Roughness in the Taklimakan Desert by Sebastiano Trevisani, Peter L. Guth

“…The different geomorphic features characterizing a landscape exhibit specific characteristics and scales of surface texture. The capability to selectively analyze specific roughness metrics at multiple spatial scales represents a key tool in geomorphometric analysis. …”
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