Data‐driven prediction of phase formation in graphene–metal systems based on phase diagram insights by Leilei Chen, Changheng Li, Kai Xu, Ruonan Zhou, Ming Lou, Yujie Du, Denis Music, Keke Chang

“…Abstract Graphene–metal (G‐M) composites have attracted tremendous interests due to their promising applications in electronics, optics, energy‐storage devices and nano‐electromechanical systems. …”
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Designing a System Architecture for Dynamic Data Collection as a Foundation for Knowledge Modeling in Industry by Edmund Radlbauer, Thomas Moser, Markus Wagner

“…A developed prototype demonstrates the practical applicability of the architecture, tested in a real industrial environment with a focus on processing speed, data integrity, and system reliability. …”
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Multi-Domain Data Integration for Plasma Diagnostics in Semiconductor Manufacturing Using Tri-CycleGAN by Minji Kang, Sung Kyu Jang, Jihun Kim, Seongho Kim, Changmin Kim, Hyo-Chang Lee, Wooseok Kang, Min Sup Choi, Hyeongkeun Kim, Hyeong-U Kim

“…To synchronize and integrate data from these different domains, we developed a Tri-CycleGAN model that utilizes three interconnected CycleGANs for bi-directional data transformation between OES, QMS, and ToF-MS. …”
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Vehicle Localization in IoV Environments: A Vision-LSTM Approach with Synthetic Data Simulation by Yi Liu, Jiade Jiang, Zijian Tian

“…These findings underscore the innovative advantages of integrating Vision-LSTM with synthetic data, highlighting its potential to overcome real-world limitations, reduce costs, and enhance accuracy and reliability for connected vehicle applications such as autonomous navigation and environmental perception.…”
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An Efficient Interpretable Visualization Method of Multidimensional Structural Data Matching Based on Job Seekers and Positions by Guoliang Si, Hengyi Lv, Hangfei Yuan, Dan Xie, Ce Peng

“…In this paper, we propose an efficient interpretable visualization method of multidimensional structural data matching based on job seekers and positions. …”
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Development and evaluation of machine learning training strategies for neonatal mortality prediction using multicountry data by Gabriel Ferreira dos Santos Silva, Roberta Moreira Wichmann, Francisco Costa da Silva Junior, Alexandre Dias Porto Chiavegatto Filho

“…This study utilized the Maternal and Neonatal Health Registry (MNHR) dataset from the National Institutes of Health (NIH), encompassing multicentric neonatal data across various countries, to evaluate the effectiveness of ML in predicting neonatal mortality risk. …”
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Harnessing AI forward and backward chaining with telemetry data for enhanced diagnostics and prognostics of smart devices by Muhammad Shoaib Farooq, Rizwan Pervez Mir, Atif Alvi, Kilian Tutusaus, Eduardo Garcia Villena, Fadwa Alrowais, Hanen Karamti, Imran Ashraf

“…The wealth of information offered by telemetry data gathered from IoT devices presents an opportunity for diagnostics and prognostics applications. …”
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An Ensemble Classification Method Based on a Stacking Strategy for Ship Type Classification with AIS Data by Lei Deng, Shichen Yang, Limin Jia, Danyang Geng

“…Traditional ship type classification methods with AIS data are often plagued by problems such as data imbalance, insufficient feature extraction, reliance on single-model approaches, or unscientific model combination methods, which reduce the accuracy of classification. …”
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From Data to Deployment: A Comprehensive Analysis of Risks in Large Language Model Research and Development by Tianshu Zhang, Ruidan Su, Anli Zhong, Minwei Fang, Yu-dong Zhang

“…Large language models (LLMs) have evolved significantly, achieving unprecedented linguistic capabilities that underpin a wide range of AI applications. However, they also pose risks and challenges such as ethical concerns, bias and computational sustainability. …”
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Optimizing Geophysical Inversion: Versatile Regularization and Prior Integration Strategies for Electrical and Seismic Tomographic Data by Guido Penta de Peppo, Michele Cercato, Giorgio De Donno

“…Additionally, we present a novel constrained inversion strategy that effectively balances prior information, minimizes data misfit, and promotes model smoothness. This approach is thoroughly compared with the state-of-the-art methods, demonstrating its superiority in maintaining model reliability and reducing dependence on subjective operator choices. …”
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Leveraging time-based spectral data from UAV imagery for enhanced detection of broomrape in sunflower by Guy Atsmon, Anna Brook, Tom Avikasis Cohen, Fadi Kizel, Hanan Eizenberg, Ran Nisim Lati

“…Results showed that the PRNN model, trained on time-series data, achieved an overall accuracy of 84.8 % and a true positive rate of 80.4 % in detecting broomrape infection, emphasizing the strength of utilizing temporal data for enhancing detection accuracies. …”
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Routing Protocols and Mobility Predictions for UAV Data Collection From Agricultural Sites: A Survey by Khemis Ben Samuel, Stephen D. Wolthusen, Julianne Sansa Otim, Roseline Nyongarwizi Akol, Mary Nsabagwa

“…Unmanned Aerial Vehicles (UAVs), also known as drones, have found applications in precision agriculture owing to their agility and remote access. …”
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Data driven resistivity zonation integrating inversion kriging and clustering for subsurface characterization in groundwater exploration by Gumilar Utamas Nugraha, Hendra Bakti, Rachmat Fajar Lubis, Asep Mulyono, Yuniarti Ulfa, Yayat Sudrajat

“…This study confirms that the combination of geoelectrical inversion, geostatistics, and clustering improves the accuracy of subsurface mapping, offering a data-driven approach applicable to complex geological environments. …”
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Vehicle Trajectory Reconstruction on Urban Traffic Network Using Automatic License Plate Recognition Data by Xinyi Qi, Yanjie Ji, Wenhao Li, Shuichao Zhang

“…Vehicle trajectory data are critical to urban active traffic management and simulation applications. …”
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Data-driven approaches for predicting Tommy John Surgery risk in major league baseball pitchers by Bosuk Kang, Minsu Park, Angel P. del Pobil, Eunil Park

“…In addition, to enhance our model’s applicability, we employ an explainable artificial intelligence technique to analyze the impacting mechanical features, such as a lowered four-seam fastball release point, that accelerate UCL deterioration, increasing TJS risk. …”
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Efficient spheroid morphology assessment with a ChatGPT data analyst: implications for cell therapy by Takuya Sakamoto, Hiroto Koma, Ayane Kuwano, Tetsuhiro Horie, Atsushi Fuku, Hironori Kitajima, Yuka Nakamura, Ikuhiro Tanida, Yujiro Nakade, Hiroaki Hirata, Yoshiyuki Tachi, Hiroshi Sunami, Daisuke Sakamoto, Sohsuke Yamada, Naoki Yamamoto, Yusuke Shimizu, Yasuhito Ishigaki, Toru Ichiseki, Ayumi Kaneuji, Satoshi Osawa, Norio Kawahara

“…Bland–Altman plots demonstrated strong agreement; most data points were within the 95% limits.Conclusion The ChatGPT Data Analyst provides a reliable and efficient alternative for assessing spheroid quality. …”
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Full cycle rice growth monitoring with dual-pol SAR data and interpretable deep learning by Ji Ge, Hong Zhang, Lu Xu, Wenjiang Huang, Jingling Jiang, Mingyang Song, Zihuan Guo, Chao Wang

“…This architecture is also applicable to other neural networks. The method is applied in Suihua City, Heilongjiang Province, China, using Sentinel-1 dual-pol data from 2022 and 2023. …”
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MassCube improves accuracy for metabolomics data processing from raw files to phenotype classifiers by Huaxu Yu, Jun Ding, Tong Shen, Min Liu, Yuanyue Li, Oliver Fiehn

“…MassCube automatically detected age, sex and regional differences when applied to the Metabolome Atlas of the Aging Mouse Brain data despite batch effects. MassCube is available at https://github.com/huaxuyu/masscube for direct use or implementation into larger applications in omics or biomedical research.…”
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Fast grouping fusion method of dual carbon monitoring data based on DBSCAN clustering algorithm by Rui Ma, Jiangbo Sha, Shuang Zhang, Dongge Zhu, Wenni Kang, Jia Liu

“…To realize the sharing and utilization of monitoring data resources, a fast group fusion method of dual-carbon monitoring data based on DBSCAN (Ensity - Based Spatial Clustering of Applications with Noise) clustering algorithm is proposed. …”
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A Prognostic Model for Newly Operated Highway Bridges Based on Censored Data and Survival Analysis by Liang Huang, Jixin Duan, Shizhan Xu, Jiapeng Zhu, Jun Liu, Danjie Niu, Li Xu, Jiangtao You, Pengtao Xue

“…The model proposed in this paper is highly applicable and reliable. Theoretically, bridge decay prediction at regional and network-wide levels can be achieved if sufficient comprehensive bridge inspection data can be collected.…”
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