Integration of deep learning and railway big data for environmental risk prediction models and analysis of their limitations by Liuhui Quan, Minjie Wang, Lyu Baihang, Zhang Ziwen, Zhang Ziwen, Zhang Ziwen

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Real-Time Fault Diagnosis of Mooring Chain Jack Hydraulic System Based on Multi-Scale Feature Fusion Under Diverse Operating Conditions by Yujia Liu, Wenhua Li, Haoran Ye, Shanying Lin, Lei Hong

“…Subsequently, the bidirectional long short-term memory (BiLSTM) layer is introduced to construct a dynamic temporal model to comprehensively capture the evolution of the fault severity. …”
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Machine learning assisted design of Fe-Ni-Cr-Al based multi-principal elements alloys with ultra-high microhardness and unexpected wear resistance by Ling Qiao, Jingchuan Zhu, Junya Inoue

“…Generalized Regression Neural Network (GRNN) showed high accuracy to construct the composition-microhardness model and was used for microhardness prediction and composition optimization. …”
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Coupling Machine Learning and Physically Based Hydrological Models for Reservoir-Based Streamflow Forecasting by Benjun Jia, Wei Fang

“…High-accuracy streamflow forecasting with long lead times can help promote the efficient utilization of water resources. However, the construction of cascade reservoirs has allowed the evolution of natural continuous rivers into multi-block rivers. …”
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Research on Multi-Step Prediction of Pipeline Corrosion Rate Based on Adaptive MTGNN Spatio-Temporal Correlation Analysis by Mingyang Sun, Shiwei Qin

“…In order to comprehensively investigate the spatio-temporal dynamics of corrosion evolution under complex pipeline environments and improve the corrosion rate prediction accuracy, a novel framework for corrosion rate prediction based on adaptive multivariate time series graph neural network (MTGNN) multi-feature spatio-temporal correlation analysis is proposed. …”
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Mapping AI ethics narratives: evidence from Twitter discourse between 2015 and 2022 by Mengyi Wei, Puzhen Zhang, Chuan Chen, Dongsheng Chen, Chenyu Zuo, Liqiu Meng

“…The framework consists of two main parts: (1) combining neural networks with large-scale language models to construct a hierarchically structured topic framework that not only extracts popular topics of public interest, but also highlights smaller, yet significant voices; (2) using narrative metaphors to achieve the integration of fragmented information across levels and topics, ultimately presenting a complete story to help the public better understand the evolution of topics within AI ethics discourse. …”
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The Analysis of Communication Strategy of Disabled Sports Information Based on Deep Learning and the Internet of Things by Wanglong Wang, Qingwen Liu, Chuan Shu

“…The ever-growing landscape of Internet of Things (IoT) technology and the evolution of deep learning algorithms have ushered in transformative changes in the communication strategy for disseminating information on disabled sports. …”
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Engine performance and emission optimization with waste cooking oil biodiesel/diesel blend using ANN and RSM techniques coupled with ACKTR-DE and HHO algorithms by Mehmet Ali Biberci, Mustafa Bahattin Çelik, Esma Ozhuner

“…Abstract In this experimental investigation, Artificial Neural Network (ANN) and Response Surface Methodology (RSM) model structures were constructed to predict and optimize the performance and exhaust emissions of a diesel engine operating on a blend of diesel fuel and waste oil biodiesel. …”
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3-Dimensional printing and bioprinting in neurological sciences: applications in surgery, imaging, tissue engineering, and pharmacology and therapeutics by Sreejita Dhar, Faraz Ahmad, Aditi Deshpande, Sandeep Singh Rana, Toufeeq Ahmed A, Swagatika Priyadarsini

“…Furthermore, the emergence of 3D bioprinting (3DBP), a fusion of 3D printing and cell biology, has created new avenues in neural tissue engineering. Effective and ethical creation of tissue-like biomimetic constructs has enabled mechanistic, regenerative, and therapeutic evaluations. …”
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An optimized spatial target trajectory prediction model for multi-sensor data fusion in air traffic management by Jian Dong, Yuan Xu, Rigeng Wu, Chengwang Xiao

“…With the evolution of air traffic safety management, the traditional single-sensor approach no longer meets the demands for spatial target surveillance. …”
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Prediction of Highway Tunnel Pavement Performance Based on Digital Twin and Multiple Time Series Stacking by Gang Yu, Shuang Zhang, Min Hu, Y. Ken Wang

“…This paper (1) establishes an MTSS prediction model with heterogeneous stacking of eXtreme gradient boosting (XGBoost), the artificial neural network (ANN), random forest (RF), ridge regression, and support vector regression (SVR) component learners after exploratory data analysis (EDA); (2) proposes a method based on multiple time series feature extraction to accurately predict the pavement performance change trend, using the highway segment as the minimum computing unit and considering multiple factors; (3) uses grid search with the k-fold cross validation method to optimize hyperparameters to ensure the robustness, stability, and generalization ability of the prediction model; and (4) constructs a digital twin for pavement performance prediction to realize the real-time dynamic evolution of prediction. …”
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Risk Assessment and Dynamic Monitoring of China’s Agricultural Investment in Countries Along the Belt and Road Under the Guidance of Cultivated Land Resources by Yameng Wang, Guanglu Zhu, Mingyue Zhang, Songxiang Wang, Yuxin Han, Linyan Ma

“…A deep neural network model is constructed to monitor the investment risk dynamically. …”
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CGD-CD: A Contrastive Learning-Guided Graph Diffusion Model for Change Detection in Remote Sensing Images by Yang Shang, Zicheng Lei, Keming Chen, Qianqian Li, Xinyu Zhao

“…However, most SSL algorithms for CD in remote sensing image rely on convolutional neural networks with fixed receptive fields as their feature extraction backbones, which limits their ability to capture objects of varying scales and model global contextual information in complex scenes. …”
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Detection and tracking of carbon biomes via integrated machine learning by S. Mohanty, S. Mohanty, L. Patara, D. Kazempour, P. Kröger

“…A deep learning model was constructed to track the seasonal and interannual evolution of the carbon biomes, wherein a feed-forward neural network was trained to assign labels to detected biomes. …”
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Hybrid Modeling of an Induction Machine to Support Bearing Diagnostics by Praneet Amitabh, Dimitar Bozalakov, Frederik De Belie

“…Finally, the difference in the responses is reduced using the neural network such that it can mimic real-world machine behavior closely. …”
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“Locality – Adaptation” Research of Hydropower Resettlement Communities in the Jinsha River Basin: A Case Study of Ludila Hydropower Station by Fang WANG, Zhuoqi LI, Haoyi XU, Jiaqi YAN

“…Through multi-source data fusion (including remote sensing, demographic statistics, and compensation records) and field investigations, the research quantitatively analyzes the “locality – adaptation” reconstruction process of resettlement communities across the three spatial dimensions of living, production, and cultural spaces.Results1) Land cover evolution: Water storage of hydropower station inundates 37.12 km2 of land (including 21.61 km² of high-quality cultivated land), resulting in a loss of 43.68% of settlement production space. 2) Adjustment of architectural space: 65.8% of dwellings in the original villages are traditional timber-earth buildings with a tile roof, which adapt to the climate environment of concentrated rainfall and sufficient sunshine in the Jinsha River Basin. 48.7% of the resettlement sites adopt unified flat roof planning, and 51.3% realize functional optimization through self-construction or renovation, with the average renovation area reaching 230.9 m2. 3) Transformation of livelihood system: The annual compensation mechanism only restores 15.79% of the cultivated land allocation before resettlement, and the proportion of compensation income drops from 82.71% in the initial stage of relocation to 33.15% after long-term resettlement, driving the adjustment of crop structure to high value-added cash crops. 4) Cultural space reconstruction: In the context of the disappearance of traditional water transport functions, the three cultural relics reconstructed in different places show a symbolic turn. …”
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Research on deep learning model for stock prediction by integrating frequency domain and time series features by Wenjie Sun, Jianhua Mei, Shengrui Liu, Chunhong Yuan, Jiaxuan Zhao

“…The main innovations of this model are reflected in three aspects: (1) Construction of a time-channel hybrid model (MultTime2dMixer) to decouple the temporal evolution and inter-channel interactions of multivariate time series. (2) A novel non-graph-based stock relation modeling approach (NoGraphMixer) is proposed, which employs a learnable attention-based mapping mechanism to dynamically capture cross-stock dependencies without relying on pre-defined or static graph structures–thereby overcoming the inflexibility of conventional graph-based relation encoders. (3) Integration of a frequency-domain complex attention model (ATFNet) to model discontinuities in both the time and frequency domains, providing a strong supplement to time-domain modeling. …”
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Identification Method of Dynamic Propagation Process of Rock Fracture Based on Ground Penetrating Radar by CHEN Jun, ZHANG Bo, ZHUANG Xingyue, SONG Zhishu, ZHENG Jun

“…ObjectiveThe stability of rock engineering structures, crucial for national major construction projects, strategic security, and environmental protection, is significantly influenced by the dynamic propagation of rock mass fractures. …”
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Investigation of Mechanical and Corrosion Behavior of ECAP Processed AA7075 Through ML, ANNW, RSM, and SA Methodologies by Majed Alinizzi, W. H. El‐Garaihy, A. I. Alateyah, Samar El‐Sanabary, Fahad Nasser Alsunaydih, Mansour Alturki, H. Abd El‐Hafez, Mohamed S. El‐Asfoury, Eman M. Zayed, Hanan Kouta

“…ABSTRACT This study employs a multi‐perspective modeling approach combining Response Surface Methodology (RSM), Machine Learning (ML), Artificial Neural Networks (ANNW), and Simulated Annealing (SA) to optimize Equal Channel Angular Pressing (ECAP) parameters for improving the mechanical and corrosion properties of AA7075 alloy. …”
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Soft actor-critic algorithm and improved GNN model in secure access control of disaggregated optical networks by Zhenqian Zhao, Yuhe Wang

“…GESAC is built on spatiotemporal modeling of evolving topologies and leverages a cross-layer spatiotemporal Graph Neural Network (GNN) to capture causal dependencies between optical path switching and access requests. …”
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