Digital mapping of Ghana’s soil properties and nutrients: performance of spline and weighted average approaches by Kora B.D. Simperegui, Anselme K.K. Kouame, Benjamin Kwesie, Prem S. Bindraban, William Adzawla, Eric Asamoah, Mohamed El Gharous

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Spatial networks reveal how forest cover decreases the spread of agricultural pests by Débora C. Rother, Leandro G. Cosmo, Julia Tavella, Fredric M. Windsor, Mariano Devoto, Darren M. Evans, Paulo R. Guimarães Jr.

“…By adjusting parameters such as pest mobility, and interaction with landscape features, our model can simulate different agricultural systems and pest behaviors, showing that forest cover can be used to control pest occurrence and that direct and indirect pathways in spatial networks can be used as a predictive tool to manage the pest spread in agricultural landscapes.…”
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Ecological and temporal drivers of human-gaur conflict in Tamil Nadu, India by Thekke Thumbath Shameer, Priyambada Routray, A. Udhayan, Rangaswamy Kanchana, Senbagapriya Sekar, Sivaranjani Shankar, Dhayanithi Vasanthakumari, Selvakumar Subramaniyam

“…This study offers critical insights into the spatial ecology of HGC and demonstrates the utility of predictive modeling for identifying high-risk areas, informing proactive mitigation strategies for conservation managers.…”
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Estimating the Relative Risks of Spatial Clusters Using a Predictor–Corrector Method by Majid Bani-Yaghoub, Kamel Rekab, Julia Pluta, Said Tabharit

“…Building on our prior research, we propose a predictive Markov chain model with an embedded corrector component. …”
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Integrative habitat analysis and multi-instance deep learning for predictive model of PD-1/PD-L1 immunotherapy efficacy in NSCLC patients: a dual-center retrospective study by Xiaoxiao Huang, Xiaoxin Huang, Yurun Xie, Kui Wang, Housheng Bai, Ruiling Ning, Xiqi Zhu, Deyou Huang, Guanqiao Jin

“…Finally, a separate PD-L1 expression dataset was used to compare the predictive performance of imaging models against PD-L1 status (positive/negative) and expression levels (high/low) to identify the optimal model for predicting immunotherapy clinical benefit. …”
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Few-shot hotel industry site selection prediction method based on meta learning algorithms and transportation accessibility by Na Li, Huaishi Wu

“…First, the initial location prediction results are obtained through the meta-model. …”
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GL-ST: A Data-Driven Prediction Model for Sea Surface Temperature in the Coastal Waters of China Based on Interactive Fusion of Global and Local Spatiotemporal Information by Ning Song, Jie Nie, Qi Wen, Yuchen Yuan, Xiong Liu, Jun Ma, Zhiqiang Wei

“…The spatiotemporal multimodal variations in sea surface temperature refer to its diverse changes across different temporal and spatial scales. Understanding and predicting these variations are crucial for climate research and marine ecosystem conservation. …”
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BEEF PRICE FORECASTING BASED ON TEMPORAL, SPATIAL AND SPACE-TIME PARAMETER INDICES by Syifa Nurul Fatimah, Ahmad Fuad Zainnuddin, Novi Mardiana, Utriweni Mukhaiyar

“…The best predictive model for forecasting beef prices is the ARIMA model. …”
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Optimizing cropland expansion for minimizing ecosystem service loss in China by Siyan Zeng, Junna Liu, Jing Ma, Yongjun Yang, Gang-Jun Liu, Fu Chen

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Seabird moult timing and duration: Implications for at-sea threat exposure worldwide by Peter Allen, Anne Peters, Zali Stiefel, Rohan H. Clarke

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Geospatial analysis of immunisation outcomes in Nigeria using a Bayesian geostatistical approach and an interactive dashboard by Chijioke Kaduru, Idim Godwin, Olamide Akeboi, Karinate Cyril-Egware, Sharon Uzoma, Maduekwe Vivian, Ebuka Nwafia, Oshodi Esther, Tijjani Habib, Shehu Abdullahi, Rehanat Amusu, Jedydah Atieno, Koko Aadum, Ojonimi Alfred, Geraldine Mbagwu, Ganiyat Eshikhena, Ugomma Nyananyo, Josephina Obande, Ezra Gayawan

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Mapping forage quality parameters based on Sentinel-2 images and auxiliary data during the senescent stage of alpine grasslands in Tibetan Plateau by Jinlong Gao, Tiangang Liang, Qisheng Feng, Zhibin He, Jing Wang, Dongmei Zhang

“…During the senescent stage, the application of remote sensing technology to understand the spatial pattern of forage quality parameters accurately is crucial for grazing management. …”
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Gradient boosting reveals spatially diverse cholesterol gene signatures in colon cancer by Xiuxiu Yang, Debolina Chatterjee, Justin L. Couetil, Ziyu Liu, Valerie D. Ardon, Chao Chen, Jie Zhang, Kun Huang, Kun Huang, Kun Huang, Travis S. Johnson, Travis S. Johnson, Travis S. Johnson

“…To evaluate the relationship between cholesterol metabolism and CC prognosis, we used the genes from this pathway in several statistical models like Cox proportional Hazard (CPH), Random Forest (RF), Lasso Regression (LR), and the eXtreme Gradient Boosting (XGBoost) to identify the genes which contributed highly to the predictive ability of all models, ADCY5, and SLC2A1. …”
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Spatial correlation guided cross scale feature fusion for age and gender estimation by Shiyi Jiang, Qing Ji, Hukui Shi, Che Chen, Yang Xu

“…Comprehensive experiments demonstrate that SCGNet achieves state-of-the-art performance with minimum Mean Absolute Error (MAE) 4.01% for age estimation on IMDB-Clean (2.9% improvement over VOLO-D1) and highest gender classification accuracy on IMDB-Clean, UTKFace, and Lagenda datasets, showing improvements in cross-scene adaptability compared to VOLO and MiVOLO models respectively. Notably, the method maintains gender discrimination accuracy under complete facial occlusion scenarios, validating the effectiveness of spatial correlation modeling for non-facial feature reasoning, maintaining 97.32% gender accuracy even with complete facial occlusion on Lagenda dataset. …”
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Comparative evaluation of machine learning models for extreme river water level forecasting in Bangladesh: Implications for flood and drought resilience by Md Touhidul Islam, Sujan Chandra Roy, Nusrat Jahan, Al-Mahmud, Md Mazharul Islam, Abdullah Al Ferdaus, Kazunori Fujisawa, A.K.M. Adham

“…This study compares nine machine learning (ML) models for predicting monthly maximum and minimum water levels at three key stations along the Old Brahmaputra River using a 34-year dataset (1990–2024). …”
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Decoding PM_2.5 Prediction in Nanning Urban Area, China: Unraveling Model Superiorities and Drawbacks Through SARIMA, Prophet, and LightGBM by Minru Chen, Binglin Liu, Mingzhi Liang, Nini Yao

“…The SARIMA model is based on time series prediction theory and performs well in some scenarios, but has limitations in dealing with non-stationary data and spatial heterogeneity. …”
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A Multi-Regional CGE Model for the Optimization of Land Resource Allocation: A Simulation of the Impact of High-Quality Development Policies in China by Luge Wen, Tiyan Shen, Yuran Huang

“…To address these gaps, this study introduces a multi-scale, multi-type China Territorial Spatial Planning Simulation Model (CTSPM). This model integrates cultivated, forest, grassland, and construction land, simulating the land use changes driven by socioeconomic impacts through price mechanisms. …”
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Immunophenotype-guided interpretable radiomics model for predicting neoadjuvant anti-PD-1 response in stage III–IV d-MMR/MSI-H colorectal cancer by Xuan Zhang, Zhenhui Li, Yiwen Zhang, Yanli Li, Xi Zhong, Wenjing Jiang, Xiaobo Chen, Zaiyi Liu, Liebin Huang, Caixia Zhang, Lizhu Liu, Ruimin You, Xiaoping Yi

“…This study aimed to develop an interpretable radiomics model guided by immunophenotypes to predict response to preoperative immunotherapy in CRC, with the goal of enabling more precise and personalized treatment strategies.Methods First, we retrospectively collected 108 patients with CRC from the center who underwent preoperative CT and RNA sequencing. …”
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Using deep convolutional neural networks to forecast spatial patterns of Amazonian deforestation by James G. C. Ball, Katerina Petrova, David A. Coomes, Seth Flaxman

“…We designed four model architectures, based on 2D CNNs, 3D CNNs, and Convolutional Long Short‐Term Memory (ConvLSTM) Recurrent Neural Networks (RNNs), to produce spatial maps that indicate the risk to each forested pixel (~30 m) in the landscape of becoming deforested within the next year. …”
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RCSAN residual enhanced channel spatial attention network for stock price forecasting by WenJie Sun, Ziyang Liu, ChunHong Yuan, Xiang Zhou, YuTing Pei, Cui Wei

“…Abstract This study proposes a stock price prediction model based on the Residual-enhanced Channel-Spatial Attention Network (R-CSAN), which integrates channel-spatial adaptive attention mechanisms with residual connections to effectively capture the multidimensional complex patterns in financial time series. …”
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