Interpretation of Bayesian-optimized deep learning models for enhancing soil erosion susceptibility prediction and management: a case study of Eastern India by Meshel Alkahtani, Javed Mallick, Saeed Alqadhi, Md Nawaj Sarif, Mohamed Fatahalla Mohamed Ahmed, Hazem Ghassan Abdo

“…Addressing this issue requires advanced predictive models that can accurately identify areas at risk and inform soil conservation strategies. …”
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Artificial intelligence-assisted magnetic resonance imaging technology in the differential diagnosis and prognosis prediction of endometrial cancer by Xinyu Qi

“…Based on the deep learning convolutional neural network (CNN) architecture residual network with 101 layers (ResNet-101), spatial attention and channel attention modules were introduced to optimize the model. …”
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4D trajectory lightweight prediction algorithm based on knowledge distillation technique by Weizhen Tang, Jie Dai, Zhousheng Huang, Boyang Hao, Weizheng Xie

“…In the distillation process, soft labels from the teacher and hard labels from actual observations jointly guide student trainingResultsIn multi-step prediction experiments, the distilled RCBAM–TCN–LSTM model achieved average reductions of 40%–60% in MAE, RMSE, and MAPE compared with the original RCBAM and TCN–LSTM models, while improving R² by 4%–6%. …”
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Spectral-Spatial Convolutional Hybrid Transformer for Hyperspectral Image Classification by Haixin Sun, Jingwen Xu, Fanlei Meng, Mengdi Cheng, Qiuguang Cao

“…However, how to integrate the spectral and spatial features effectively after dimensionality reduction while retaining key information remains to be optimized further. …”
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Advancement of a diagnostic prediction model for spatiotemporal calibration of earth observation data: a case study on projecting forest net primary production in the mid-latitude... by Eunbeen Park, Hyun-Woo Jo, Gregory Scott Biging, Jong Ahn Chun, Seong Woo Jeon, Yowhan Son, Florian Kraxner, Woo-Kyun Lee

“…This study introduced a diagnostic prediction concept as a generalized modeling framework for enhancing modeling precision and interpretability and demonstrate a case study of estimating forest net primary production (NPP) in a mid-latitude region (MLR) by developing a diagnostic NPP diagnostic prediction model (DNPM). …”
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Mechanism and Control of Rockburst Induced by Draining Spatial Islands and Squaring by Yunhai Cheng, Fenghui Li, Xiufeng Zhang, Gangwei Li, Le Xu

“…This study examines the 2202 gob-side working face of a coal mine in Inner Mongolia as a case study. A stress evolution model was built for the static-stress spatial islands formed by drainage regions and goafs based on the spatial relationships between drainage regions and goafs. …”
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Spatial–Temporal Analysis of Greenness and Its Relationship with Poverty in China by Wentong Xie, Yong Ge, Nicholas A. S. Hamm, Giles M. Foody, Zhoupeng Ren

“…Using MODIS Leaf Area Index (LAI) data from 2000 to 2020, the spatial–temporal changes in greenness were obtained using the Bayesian spatial–temporal model (BYM). …”
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Western North Pacific Monsoon Gyres: A New Identification Approach, Interannual Variability, and Seasonal Prediction by Yuqi Zang, Haikun Zhao, Philip J. Klotzbach, Chao Wang, Liguang Wu

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A flexible framework for local-level estimation of the effective reproductive number in geographic regions with sparse data by Md Sakhawat Hossain, Ravi Goyal, Natasha K. Martin, Victor DeGruttola, Mohammad Mihrab Chowdhury, Christopher McMahan, Lior Rennert

“…Methods To overcome this challenge, we propose a two-step approach that incorporates existing $$\:{R}_{t}$$ estimation procedures (EpiEstim, EpiFilter, EpiNow2) using data from geographic regions with sufficient data (step 1), into a covariate-adjusted Bayesian Integrated Nested Laplace Approximation (INLA) spatial model to predict $$\:{R}_{t}$$ in regions with sparse or missing data (step 2). …”
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Trends, spatial distribution and determinants of maternal home deliveries in Zambia by Peter Mumba, Thomas O. Miyoba, Emmanuel Musonda, Mwangala Mulasikwanda, Samson Shumba

“…The study aimed at analyzing trends, spatial distribution and determinants of maternal home deliveries in Zambia between 1992 and 2018. …”
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Prediction of Complex Observed Shear Wave Splitting Patterns at Ryukyu Subduction Zone Using a Strong Intra‐Slab Anisotropy Model by Sharmila Appini, Jiaxuan Li, Hao Hu, Neala Creasy, Leon Thomsen, Joseph McNease, Yingcai Zheng

“…For the same earthquake, the measured splitting patterns also vary spatially across the southwest Japan. Using full‐wave seismic modeling, we showed that a dipping slab with ∼30% shear anisotropy of the tilted transverse isotropy (TTI) type, with a symmetry axis perpendicular to the slab interface, can predict the observed delay times and polarization rotation. …”
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Optimization of Reservoir Operation using a Bioinspired Metaheuristic Based on the COVID-19 Propagation Model by Alireza Donyaii, Amirpouya Sarraf

“…The climatic variables downscaled and predicted by the Bias Correction Spatial Disaggregation (BCSD) method of MIROC-ESM model, was introduced into the Extreme Learning Machine (ELM) modelto evaluate the future runoff flowing into the reservoir. …”
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Potential Distribution Prediction of Terminalia chebula Retz. in China Under Current and Future Climate Scenarios by Zhang‐Hong Dong, Hua Jiang, Wei Zhang, Jianhua Wu, Yanping Yang, Taoming Yang, Jiangping Zhao, Cunzhen Luo, Xiaoxia Yang, Guilin Li

“…Utilizing GIS and MaxEnt model, we predicted the spatial distribution of Terminalia chebula Retz. in China for the current and for the future (2050s and 2070s) under the RCP4.5 and RCP8.5 representative concentration pathways. …”
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Urban Canopy Parameters’ Computation and Evaluation in an Indian Context Using Multi-Platform Remote Sensing Data by Kshama Gupta, Bhoomika Ghale, Ashutosh Bhardwaj, Anshika Varshney, Shweta Khatriker, Vinay Kumar, Prasun Kumar Gupta, Pramod Kumar

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Implementation of stochastic signal processing algorithms in radar CAD by M. Yu. Konopel'kin, S. V. Petrov, D. A. Smirnyagina

“…Radar CAD provides extensive opportunities for creating simulation models for developing the hardware-software complex of radar algorithms, which take into account the specific conditions of aerospace environment observation. …”
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Does spatial information improve forecasting of influenza-like illness? by Gabrielle Thivierge, Aaron Rumack, F. William Townes

“…There is also clear within-season and between-season variability in the effect of spatial information on prediction accuracy.…”
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Spatial Correlation and Ergodic Capacity of MIMO Channel in Reverberation Chamber by Xiaoming Chen

“…However, the MIMO channel in the reverberation chamber has not been fully studied before. In this paper, the spatial correlation of the MIMO channel in the chamber is studied by comparing the measured channel with two popular MIMO channel models. …”
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Determining disease intervention strategies using spatially resolved simulations. by Mark Read, Paul S Andrews, Jon Timmis, Richard A Williams, Richard B Greaves, Huiming Sheng, Mark Coles, Vipin Kumar

“…Here we present a new methodology to simulate inflammatory disease manifestation and test potential intervention strategies in silico using agent-based computational models. Simulations created using this methodology have explicit spatial and temporal representations, and capture the heterogeneous and stochastic cellular behaviours that lead to emergence of pathology or disease resolution. …”
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Spatial Appraisal of Seasonal Water Yield of the Sokoto-Rima Basin by Shakirudeen Odunuga, Mayowa Fasona, Saheed Adekunle Raji

“…Understanding the dynamics of water yield as relates to water balance is vital for the functioning of ecosystems as well as model-based computation of their respective ecosystem services. …”
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The regulation of ant colony foraging activity without spatial information. by Balaji Prabhakar, Katherine N Dektar, Deborah M Gordon

“…We found that correlations between observed rates of returning foragers and simulated rates of outgoing foragers, using our model, were similar to those in the data. Our simple stochastic model shows how the regulation of ant colony foraging can operate without spatial information, describing a process at the level of individual ants that predicts the overall foraging activity of the colony.…”
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