Generative Artificial Intelligence-Enabled Facility Layout Design Paradigm by Fuwen Hu, Chun Wang, Xuefei Wu

“…The convolutional knowledge graph embedding (ConvE) method is employed for link prediction, converting entities and relationships into low-dimensional vectors to infer optimal spatial arrangements while addressing data sparsity through negative sampling. …”
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Matching of Observation Footprints in the FY-3G MWRI-RM Using BGI by Ke Chen, Bowen Cai, Wei Han, Zihao Suo

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Subsurface Geological Profile Interpolation Using a Fractional Kriging Method Enhanced by Random Forest Regression by Qile Ding, Yiren Wang, Yu Zheng, Fengyang Wang, Shudong Zhou, Donghui Pan, Yuchun Xiong, Yi Zhang

“…The results indicate that the proposed model reduces prediction errors and enhances spatial prediction reliability compared to other models. …”
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Integrating PROSPECT-D physics and adversarial domain adaptation resnet for robust cross-ecosystem plant traits estimation by Hui Zhang, Haoxuan Su, Tie Shen, Guangyao Sun, Qi Wang

“…Challenges such as data heterogeneity, domain shifts, and sparse in situ measurements further hinder model generalization. To address these limitations, this study developed PPADA-Net, a novel framework integrating PROSPECT-D radiative transfer modeling with adversarial domain adaptation for robust cross-ecosystem plant trait prediction. …”
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Investigating socioeconomic deprivation and antibiotic prescribing among older medicare patients using an instrumental variable approach by Mayar Al Mohajer, David Slusky, David Nix, Catia Nicodemo

“…The IV analysis then examined the relationship between predicted SDI and antibiotic days supplied (ln). Linear regression models estimated associations between SDI and its components, and antibiotic days supplied, adjusting for prescriber, beneficiary, and geographic factors. …”
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A Comprehensive Review on the Application of 3D Convolutional Neural Networks in Medical Imaging by Satyam Tiwari, Goutam Jain, Dasharathraj K. Shetty, Manu Sudhi, Jayaraj Mymbilly Balakrishnan, Shreepathy Ranga Bhatta

“…CNNs use specific filters to find spatial and temporal relationships in images, making understanding and interpreting them easier. …”
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Optimal selection of high-production well targets for fault-controlled fractured-vuggy reservoir in Shunbei oil and gas field, Tarim Basin by Lijun GAO, Haiying LI, Wei GONG, Wei YANG, Hongyan LI

“…This model, based on the "source-connected faults + bead-string + deep chaotic high-amplitude background", provided a systematic framework for reservoir prediction and target selection. …”
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Monitoring Anthropogenically Disturbed Parcels with Soil Erosion Dynamics Change Based on an Improved SegFormer by Zhenqiang Li, Jialin Li, Jie Li, Zhangxuan Li, Kuncheng Jiang, Yuyang Ma, Chuli Hu

“…The PR–MLP module improves feature extraction by capturing spatial contextual information, while the CSBC module enhances boundary prediction through high– and low–level semantic guidance. …”
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Multiscale Materials Imaging and Spectroscopy for Battery Materials by Youngwoo Choi, Gumin Kang, Seonghyun Kim, Yoonhan Cho, Jaewhan Oh, Dongho Kim, Jacob Choe, Jong Min Yuk, Pyuck‐Pa Choi, Yongsoo Yang, Sung‐Yoon Chung, Chi Won Ahn, Jongwoo Lim, Seungbum Hong

“…Moreover, the integration of machine learning accelerates data processing, enabling multiscale correlations and predictive modeling. The review underscores the necessity of multiscale approaches to optimize battery performance, safety, and lifespan, showcasing how emerging methodologies contribute to next‐generation energy storage technologies.…”
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Crack-ConvT Net: A Convolutional Transformer Network for Crack Segmentation in Underwater Dams by Pengfei Shi, Hongzhu Chen, Zaiming Geng, Xinnan Fan, Yuanxue Xin

“…Firstly, a Global Information Aggregation Block is introduced to enhance the model’s ability to capture the spatial distribution of cracks by leveraging multiscale pooling and channel expansion strategies, which improve global context awareness while preserving high-resolution details. …”
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KSC-Net: a biologically inspired spatio-temporal correlation network for video-based human action recognition by Hui Ma, Xuelian Ma

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Tracking green space along streets of world cities by Giacomo Falchetta, Ahmed T Hammad

“…Based on our trained and cross-validated non-parametric model, we conduct spatial sampling and prediction in 190 large cities distributed across twenty regions and estimate local to continental GVI trends between 2016–2023. …”
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Assessing Climate Change Impacts on Cropland and Greenhouse Gas Emissions Using Remote Sensing and Machine Learning by Nehir Uyar, Azize Uyar

“…The results revealed a strong correlation between agricultural expansion and increased C and N₂O emissions, with RF and GBT models demonstrating superior predictive accuracy. …”
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A Systematic Review of Marine Habitat Mapping in the Central-Eastern Atlantic Archipelagos: Methodologies, Current Trends, and Knowledge Gaps by Marcial Cosme De Esteban, Fernando Tuya, Ricardo Haroun, Francisco Otero-Ferrer

“…Harmonized protocols and regional cooperation are needed to improve data interoperability and predictive modeling.…”
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Trends of Soil and Solution Nutrient Sensing for Open Field and Hydroponic Cultivation in Facilitated Smart Agriculture by Md Nasim Reza, Kyu-Ho Lee, Md Rejaul Karim, Md Asrakul Haque, Emmanuel Bicamumakuba, Pabel Kanti Dey, Young Yoon Jang, Sun-Ok Chung

“…Key technologies include electrochemical and optical sensors, Internet of Things (IoT)-enabled monitoring, and the integration of machine learning (ML) and artificial intelligence (AI) for predictive modeling. Blockchain technology is also emerging as a tool to enhance transparency and traceability in nutrient management, promoting compliance with environmental standards and sustainable practices. …”
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Numerical Simulation of Turbopump Inducer Cavitating Behavior by Coutier-Delgosha O., Morel P., Fortes-Patella R., Reboud JL.

“…<p>In the present study a numerical model of 3D cavitating flows is proposed. It is applied to investigate the behavior of a spatial turbopump inducer in noncavitating and cavitating conditions. …”
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A Hybrid Regression–Kriging–Machine Learning Framework for Imputing Missing TROPOMI NO₂ Data over Taiwan by Alyssa Valerio, Yi-Chun Chen, Chian-Yi Liu, Yi-Ying Chen, Chuan-Yao Lin

“…In contrast, models using only static land cover inputs performed poorly (<i>r</i>² < 0.58), emphasizing the limited predictive capacity of such variables in isolation. …”
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Downscaling and Gap-Filling GRACE-Based Terrestrial Water Storage Anomalies in the Qinghai–Tibet Plateau Using Deep Learning and Multi-Source Data by Jun Chen, Linsong Wang, Chao Chen, Zhenran Peng

“…In this study, we present a novel downscaling framework integrating temporal gap compensation and spatial refinement to a 0.25° resolution through Gated Recurrent Unit (GRU) neural networks, an architecture optimized for univariate time series modeling. …”
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Assessing climate-driven phenological responses of tomato crops under future climate change trajectories: A Central India perspective by Pashupati Nath Singh, Prashant K. Srivastava, Bhawana Sharma, R.K. Mall

“…Model performance showed robust agreement with observed yields (R = 0.78), with validation metrics—MAE = 5.9, RMSE = 6.93, and Bias = -1.43—demonstrating consistent predictive accuracy with slight underestimation. …”
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Edge-aware transformer for coastal raft aquaculture extraction in optical remote sensing imagery by Hua Su, Yuxin Liu, Zhanchao Huang, An Wang, Wenjun Hong, Junchao Cai

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