Climate and anthropogenic activities control spatiotemporal dynamics of topsoil inorganic carbon in China by Qiongyu Zhang, Jianxing Zhu, Qiufeng Wang, Guirui Yu, Cong-Qiang Liu

“…In 2020, China’s top 30 cm SIC stock was 15.15 Pg C, with the highest density in desert. Spatial patterns followed a northwest-to-southeast gradient shaped mainly by climate and soil properties. …”
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A Feasibility Study on Utilizing Remote Sensing Data to Monitor Grape Yield and Berry Composition for Selective Harvesting by Leeko Lee, Andrew Reynolds, Briann Dorin, Adam Shemrock

“…Moran’s global index and map analysis were used to determine spatial clustering patterns and correlations between variables. …”
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Left Insula and Right Middle Temporal Gyrus Dominate Cortical Network Discriminating Arousal‐dependent Emotions by Na Clara Pan, Runshi Gao, Kai Ma, Liang Qiao, Duanyu Ni, Tao Yu, Yuping Wang

“…Further, the separated network patterns related to emotional discrimination are face‐independent. …”
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Evolution Law of Longitudinal Deformation Curve and Release Coefficient of Viscoelastic Rock by WANG Jiachen, MENG Lingzan, ZHANG Dingli, LU Song, WEN Ming

“…The influences of different factors on the longitudinal deformation behavior of viscoelastic rock as well as the spatiotemporal evolution patterns have been revealed through numerical simulations. …”
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A global land-use data cube 1992–2020 based on the Human Appropriation of Net Primary Production by Sarah Matej, Florian Weidinger, Lisa Kaufmann, Nicolas Roux, Simone Gingrich, Helmut Haberl, Fridolin Krausmann, Karl-Heinz Erb

“…Advanced understanding of patterns and dynamics of land use is vital for assessing impacts on these system components and for developing strategies to ensure sustainability. …”
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Ecosystem services scenario simulation in Guangzhou based on the FLUS-InVEST model by Dafang Wu, Jizhen Mo, Lechun Zeng, Ping Zhou, Muyun Xie, Haobin Yuan

“…This paper simulates the natural, ecological and development scenarios of Guangzhou in 2035 using the FLUS model based on LUCC of Guangzhou from 2015 to 2020; on top of the three scenarios, calculates the physical quantities of three ecosystem services—annual water yield, habitat quality, and carbon storage, through the InVEST model; and uses the GeoDetector model to identify the influencing Drivers. (1) Compared to 2020, the different land use types will change differently under the three scenarios in 2035; (2) The spatial distribution of ecosystem services in Guangzhou for the years 2020 and 2035 show similar patterns across three scenarios; (3) Based on the analysis of the driving factors behind Land Use and Land Cover Change (LUCC) in Guangzhou, it has been observed that population density has the most significant impact on LUCC.…”
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Intratumoral heterogeneity and drug resistance in cancer by Yue-Chun Fu, Shao-Bo Liang, Min Luo, Xue-Ping Wang

“…Abstract Intratumoral heterogeneity is the main cause of tumor treatment failure, varying across disease sites (spatial heterogeneity) and polyclonal properties of tumors that evolve over time (temporal heterogeneity). …”
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Knowledge Graph-Augmented ERNIE-CNN Method for Risk Assessment in Secondary Power System Operations by Xiang Huang, Ping Li, Ye Wang, Xuchao Ren, Zhenbing Zhao, Gang Li

“…Second, an improved ERNIE-CNN model is developed, incorporating an attention mechanism to effectively fuse semantic features and spatial patterns from operational texts. The experimental results on a dataset of 3240 secondary operation records demonstrate the model’s superior performance, achieving precision, recall, and F1-scores of 0.878, 0.861, and 0.869, respectively, outperforming benchmarks like BERT. …”
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Global Gridded Crop Production Dataset at 10 km Resolution from 2010 to 2020 by Xingli Qin, Bingfang Wu, Hongwei Zeng, Miao Zhang, Fuyou Tian

“…This dataset reveals spatiotemporal distribution patterns of global crop production and contributes to understanding mechanisms driving changes in crop production. …”
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Interannual Variation of the Surface Temperature of Tropical Forests from Satellite Observations by Huilin Gao, Shuai Zhang, Rong Fu, Wenhong Li, Robert E. Dickinson

“…As a result of the variability of cloud cover and the corresponding reduction of solar radiation, the afternoon LST anomalies tend to vary more than the morning LST anomalies. The dominant spatial and temporal patterns for interseasonal variations of the LST anomalies over the tropical rainforest were analyzed. …”
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Effect of the Sampling of a Dataset in the Hyperparameter Optimization Phase over the Efficiency of a Machine Learning Algorithm by Noemí DeCastro-García, Ángel Luis Muñoz Castañeda, David Escudero García, Miguel V. Carriegos

“…Also, a level of gain is assigned to each partition allowing us to study patterns and allocate whose samples are more profitable. …”
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Trajectory Prediction and Intention Recognition Based on CNN-GRU by Jinghao Du, Dongdong Lu, Fei Li, Ke Liu, Xiaolan Qiu

“…Accurate trajectory prediction and intention recognition are essential for effective decision-making, as different intents manifest in distinct trajectory patterns. This study focuses on trajectory prediction and intention recognition for airborne moving targets by proposing an integrated approach that leverages trajectory data for both tasks. …”
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Grid-Free Modelling Based on the Finite Particle Method for Incompressible Viscous Flow Problems by Yu Lu, Ankang Hu, Xin Chang, Ziying Li, Yabo Wei

“…With the proposed finite particle technique, problems associated with the viscous free surface flow which contains the study on the liquid sloshing in tanks with low volumetric fluid type, solitary waves movement, and interaction with a vertical wall in numerical flume as well as the vortex patterns of the ship rolling damping are circumvented. …”
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Merging Multiple System Perspectives: The Key to Effective Inland Shipping Emission-Reduction Policy Design by Solange van der Werff, Fedor Baart, Mark van Koningsveld

“…We can subsequently tie observations in one perspective (for example, large-scale spatial patterns on a map) to supporting explanations based on another perspective (for example, water currents, vessel speeds, or engine ages and their contributions to emissions). …”
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A dual-core model for ENSO diversity: unifying model hierarchies for realistic simulations by Jinyu Wang, Xianghui Fang, Nan Chen, Bo Qin, Mu Mu, Chaopeng Ji

“…Meanwhile, the nonlinear model assimilates pseudo-observations from the stochastic model while resolving key air-sea interactions, such as oceanic feedback balances and spatial patterns of sea surface temperature anomalies during El Niño peaks. …”
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Developing an ensemble machine learning framework for enhanced climate projections using CMIP6 data in the Middle East by Younes Khosravi, Taha B.M.J. Ouarda, Saeid Homayouni

“…Abstract Climate change in the Middle East has intensified with rising temperatures, shifting rainfall patterns, and more frequent extreme events. This study introduces the Stacking-EML framework, which merges five machine learning models three meta-learners to predict maximum temperature, minimum temperature, and precipitation using CMIP6 data under SSP1-2.6, SSP2-4.5, and SSP5-8.5. …”
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BathyFormer: A Transformer-Based Deep Learning Method to Map Nearshore Bathymetry with High-Resolution Multispectral Satellite Imagery by Zhonghui Lv, Julie Herman, Ethan Brewer, Karinna Nunez, Dan Runfola

“…The model learns to predict water depths by analyzing the spectral signatures and spatial patterns present in the multispectral imagery. …”
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Anomaly Detection Based on Graph Convolutional Network–Variational Autoencoder Model Using Time-Series Vibration and Current Data by Seung-Hwan Choi, Dawn An, Inho Lee, Suwoong Lee

“…To address this issue, we employ a semi-supervised learning approach that relies solely on normal data to effectively detect abnormal patterns, overcoming the limitations of conventional methods. …”
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Classifying the Shapes of Buildings by Combining Distance Field Enhancement and a Convolution Neural Network by Xinyan Zou, Min Yang, Siyu Li, Hai Hu

“…The shape classification of building objects is crucial in fields such as map generalization and spatial queries. Recently, convolutional neural networks (CNNs) have been used to capture high-level features and classify building shape patterns based on raster representations. …”
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Housing Market Trends and Affordability in Central Europe: Insights from the Czech Republic, Slovakia, Austria, and Poland by Jitka Matějková, Alena Tichá

“…The study integrates real estate platform data with official statistics and employs spatial mapping and exploratory econometric testing to identify affordability patterns and disparities. …”
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