A method for spatial interpretation of weakly supervised deep learning models in computational pathology by Abhinav Sharma, Bojing Liu, Mattias Rantalainen

“…Such information is also needed for any further spatial interpretation of predictions from such models. …”
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Surrogate model for calculating spatial magnetic track forces in permanent magnet levitation transportation by LIU Haodong, FENG Yang, ZHAO Chunfa, LUO Shihui, SONG Xiaolin

“…Developing a method for rapidly and accurately calculating magnetic forces, which considers multi-degree-of-freedom spatial attitudes, is essential for the dynamic prediction and assessment of PML vehicles. …”
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Spatial-Temporal Distribution Prediction of Electric Vehicle Charging Load Considering Charging Behavior and Real-Time SOC by ZHANG Linjuan, LI Wenfeng, XU Changqing, GUO Jianyu, ZHANG Xiawei, YUAN Jia, WANG Yaoqiang

“…[Methods] The influence of traffic conditions and ambient temperature on EV energy consumption and charging behavior is analyzed，and road traffic network and comprehensive energy consumption models are established. Based on the user's travel chain，the user's travel characteristics are analyzed，the shortest time method is used to plan the driving path，and a spatial-temporal distribution prediction model of the EV charging load is built considering the charging queue time and real-time SOC. …”
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Spatial age-stratified epidemiological model with applications to South African COVID-19 pandemic by Raeesa Manjoo-Docrat, Nada Abdelatif, Jenny Holloway, Nontembeko Dudeni-Tlhone, Claudia Dresselhaus, Elona Mbayise, Charl Janse van Rensburg, Inger Fabris-Rotelli, Pravesh Debba, Sibusisiwe Makhanya

“…Results: The spatial age-stratified model produced more biologically plausible and accurate predictions compared to non-stratified models investigated. …”
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Temporal and Spatial Variation in Habitat Quality in Guangxi Based on PLUS-InVEST Model by Chuntian Pan, Jun Wen, Jianing Ma

“…This study systematically analyzes the spatial and temporal dynamics of land use and habitat quality in Guangxi from 2000 to 2020 using the PLUS-InVEST model and simulates future scenarios for 2030. …”
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Spatial Modelling Analysis for Potential Expansion of Cipanas City Candidate Area as Autonomous City by Lili Somantri

“…Therefore, this research aimed to address the proposal by conducting spatial modelling analysis for potential expansion of Cipanas as autonomous city, using Geographic Information System (GIS) method. …”
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Path-Loss Channel Models for Receiver Spatial Diversity Systems at 2.4 GHz by Abdulmalik Alwarafy, Ahmed Iyanda Sulyman, Abdulhameed Alsanie, Saleh A. Alshebeili, Hatim M. Behairy

“…Our empirical data is used to propose spatial diversity propagation path-loss channel models using the log-distance and the floating intercept path-loss models. …”
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Spatially Explicit Model for Assessing the Impacts of Groundwater Protection Measures in the Vicinity of the Hranice Abyss by Jozef Sedláček, Hana Vavrouchová, Kryštof Chytrý, Ondřej Ulrich, Petra Oppeltová, Milan Geršl, Kristýna Kohoutková, Radim Klepárník, Petr Kučera, Vítězslav Vlček, Jana Šimečková, Eva Žallmannová

“…The model employs a multi-criteria decision analysis, integrated with hydrological modeling and a high-resolution random forest-based prediction algorithm, to downscale land surface temperature (LST) in order to obtain high-resolution 1 × 1 m spatial results. …”
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SCS-Net: Stratified Compressive Sensing Network for Large-Scale Crowd Flow Prediction by Xiaoyong Tan, Kaiqi Chen, Min Deng, Baoju Liu, Zhiyuan Zhao, Youjun Tu, Sheng Wu

“…However, achieving accurate and efficient prediction remains challenging. Most existing models overlook spatial heterogeneity, employing unified parameters to fit diverse crowd flow patterns across different spatial units, which limits their accuracy. …”
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DGL-STFA: Predicting lithium-ion battery health with dynamic graph learning and spatial–temporal fusion attention by Zheng Chen, Quan Qian

“…This combination enables DGL-STFA to effectively model both dynamic spatial relationships and long-term temporal dependencies, enhancing SOH prediction accuracy. …”
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Genetic Programming-Based Prediction Model for Microseismic Data by Man Wang, Hongwei Zhou, Dongming Zhang, Yingwei Wang, Weihang Du, Beichen Yu

“…Therefore, we collected a large amount of microseismic data obtained from the working faces of Shoushan Mine of Pingdingshan Coal Group in China and filtered the data, then we constructed a prediction model of microseismic data based on underground spatial three-dimensional coordinates using genetic programming (GP), which can realize real-time monitoring and disaster warning of microseismic signals. …”
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Spatial Analysis of the Effects of Women’s Socioeconomic Status on Divorce Rates in Turkey by Ayça Özekin, Burcu Süküti

“…In the study, the relationship between the socioeconomic status indicators of women in Turkey and the divorce rates were examined by estimating the Spatial Autocorrelation (SAR) and Spatial Error (SEM) Models for 26 sub-regions in the Turkish Statistical Regional Units Classification (NUTS) Level 2. …”
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Research on rock burst prediction based on an integrated model by Junming Zhang, Qiyuan Xia, Hai Wu, Sailei Wei, Zhen Hu, Bing Du, Yuejing Yang, Huaixing Xiong

“…Additionally, the sparrow search algorithm (SSA) is employed to optimize hyperparameters, further improving the model’s performance. Unlike traditional approaches that rely on time-axis-based analysis, this study uses the working face advancement distance as the basis for prediction, which better reveals the potential spatial correlations of rockburst occurrences, aligning with engineering practice needs.Validation using microseismic monitoring data from a coal mine demonstrates that the proposed model achieves a prediction accuracy of 93.62% and an F1-score of 93.54%. …”
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Increasing neural network robustness improves match to macaque V1 eigenspectrum, spatial frequency preference and predictivity. by Nathan C L Kong, Eshed Margalit, Justin L Gardner, Anthony M Norcia

“…They also suggest that it may be useful to penalize slow-decaying eigenspectra or to bias models to extract features of lower spatial frequencies during task-optimization in order to improve robustness and V1 neural response predictivity.…”
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Coupling coordination between agricultural carbon emission efficiency and food security in China: The spatial-temporal evolution and prediction. by Xixian Zheng, Wenmei Liao

“…Additionally, a Combination Forecasting Model predicts CCD trends through 2030. The findings indicate positive trends in both ACEE and FS, albeit with significant regional disparities and a notable lag of FS behind ACEE improvement. …”
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Prediction of the daily spatial variation of stem water potential in cherry orchards using weather and Sentinel-2 data by Francisco Zambrano, Abel Herrera, Mauricio Olguín, Miro Miranda, Jesica Garrido, Andrea Miyasaka Almeida

“…The primary goal of this work is to predict the daily spatial variation of Ψs using machine learning models. …”
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Spatial-temporal Differentiation and Prediction of Extreme Weather Events and Vegetation Cover in Guizhou Province Under Climate Change by LI Xi'nan, HE Linhong, XUE Lianqing

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Integration of single-nuclei and spatial transcriptomics to decipher tumor phenotype predictive of relapse-free survival in Wilms tumor by Ran Yang, Ran Yang, Lulu Xie, Rui Wang, Yi Li, Yifei Lu, Baihui Liu, Shuyang Dai, Shan Zheng, Shan Zheng, Kuiran Dong, Kuiran Dong, Rui Dong, Rui Dong

“…A prognostic ensemble machine learning model was constructed based on the Scissor+ tumor signature to accurately predict patient RFS. …”
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Spatial predictions of soil moisture across a longitudinal gradient in semiarid ecosystems using UAV and RGB sensors by Alexander Hernandez, Efrain Duarte, Peter Porter, Holden Brecht

“…Texture metrics (‘mean’ and ‘entropy’), and the Excess Green (ExG) index had high predictive power while RGB bands performed poorly. Unlike Idaho and Montana, the spatial predictions for Utah and California showed high reliability (α < 0.01). …”
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Spatially explicit metrics improve the evaluation of species distribution models facing sampling biases by Claudio A. Bracho-Estévanez, Salvador Arenas-Castro, Juan P. González-Varo, Pablo González-Moreno

“…Furthermore, most predictions rely only on non-spatial metrics such as the AUC and the TSS to evaluate model performance. …”
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