Ongoing invasions by American bullfrogs and red-eared sliders in the Republic of Korea by Desiree Andersen, Amaël Borzée, Yikweon Jang

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Prediction of Land Use Change and Carbon Storage in Lijiang River Basin Based on InVEST-PLUS Model and SSP-RCP Scenario by Jing Jing, Feili Wei, Hong Jiang, Zhantu Chen, Shuang Lv, Tengfang Li, Weiwei Li, Yi Tang

“…Previous studies have not combined different climate scenarios and land use patterns to predict carbon storage. Using scenarios from both the InVEST-PLUS model and SSP-RCP, combined with multi-source remote sensing data, this study takes the Lijiang River Basin as the study area to explore the dynamic changes in land use and carbon storage under different climate scenarios. …”
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Post-Disaster Recovery Effectiveness: Assessment and Prediction of Coordinated Development in the Wenchuan Earthquake-Stricken Areas by Liang Zhao, Chunmiao Zhang, Xia Zhou

“…By constructing a framework to assess post-disaster coordinated development, this study utilized the entropy weight method and mean-variance method for the comprehensive weighting of evaluation indicators. The gray system prediction model G(1,1) was used to forecast the coordinated development levels of the three cities from 2019 to 2025. …”
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The microenvironment cell index is a novel indicator for the prognosis and therapeutic regimen selection of cancers by Xian-Yan Yang, Nian Chen, Qian Wen, Yu Zhou, Tao Zhang, Ji Zhou, Cheng-Hui Liang, Li-Ping Han, Xiao-Ya Wang, Qing-Mei Kang, Xiao-Xia Zheng, Xue-Jia Zhai, Hong-Ying Jiang, Tian-Hua Shen, Jin-Wei Xiao, Yu-Xin Zou, Yun Deng, Shuang Lin, Jiang-Jie Duan, Jun Wang, Shi-Cang Yu

“…Furthermore, combined with the spatial distribution characteristics of the six types of MCs, an MCI-enhanced (MCI-e) model was constructed, which could predict the prognosis of the TNBC patients more accurately. …”
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Spatial Position Reasoning of Image Entities Based on Location Words by Xingguo Qin, Ya Zhou, Jun Li

“…The incorporation of spatial position terms into the model was observed to elevate the average predictive accuracy by approximately three percentage points.…”
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Global distribution prediction and ecological conservation of basking shark (Cetorhinus maximus) under integrated impacts by Runlong Sun, Kaiyu Liu, Wenhao Huang, Xiao Wang, Hongfei Zhuang, Zongling Wang, Zhaohui Zhang, Linlin Zhao

“…This study employs various environmental variables and distribution data to construct a global species distribution model for basking sharks, predicting their distribution patterns under current and future climate scenarios. …”
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A dissimilarity-adaptive cross-validation method for evaluating geospatial machine learning predictions with clustered samples by Yanwen Wang, Mahdi Khodadadzadeh, Raúl Zurita-Milla

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Evaluation and Prediction of Wind Power Utilization Efficiency Based on Super-SBM and LSTM Models: A Case Study of 30 Provinces in China by Chengyu Li, Qunwei Wang, Peng Zhou

“…This study establishes the improved super-efficiency slack-based measure (Super-SBM) model and long short-term memory (LSTM) network models, systematically and comprehensively measures and predicts the wind power utilization efficiency of 30 regions in China from 2013 to 2020, and explores regional differences in wind power utilization efficiency. …”
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Slope Deformation Prediction Combining Particle Swarm Optimization-Based Fractional-Order Grey Model and <i>K</i>-Means Clustering by Zhenzhu Meng, Yating Hu, Shunqiang Jiang, Sen Zheng, Jinxin Zhang, Zhenxia Yuan, Shaofeng Yao

“…Additionally, we employ a <i>k</i>-means clustering technique to account for both temporal and spatial variations in multi-point monitoring data, which improves the model’s ability to capture the relationships between monitoring points and increases prediction relevance. …”
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Optimizing fully-efficient two-stage models for genomic selection using open-source software by Javier Fernández-González, Julio Isidro y Sánchez

“…Two-stage models, preferred for their simplicity and efficiency, first calculate adjusted genotypic means accounting for spatial variation within each environment, then use these means to predict GEBVs. …”
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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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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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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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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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Bifurcation Branch in a Spatial Heterogeneous Predator–Prey Model with a Nonlinear Growth Rate for the Predator by Lei Kong

“…A strongly coupled predator–prey model in a spatially heterogeneous environment with a Holling type-II functional response and a nonlinear growth rate for the predator is considered. …”
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Machine learning-assisted multi-dimensional transcriptomic analysis of cytoskeleton-related molecules and their relationship with prognosis in hepatocellular carcinoma by Yuxuan Li, Mingbo Cao, Xiaorui Su, Gaoyuan Yang, Yupeng Ren, Zhiwei He, Zheng Shi, Ziyi Hu, Guirong Liang, Qi Zhang, Zhicheng Yao, Meihai Deng

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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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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

“…We investigate whether the inclusion of data about influenza activity in neighboring states can improve point predictions and distribution forecasting of influenza-like illness (ILI) in each US state using statistical regression models. …”
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Relationships between abundances of breeding ducks and attributes of Canadian prairie wetlands by Blake Bartzen, Kevin W. Dufour, Mark T. Bidwell, Michael D. Watmough, Robert G. Clark

“…In regions where duck densities were high, there were more ducks per pond; conversely, there were fewer ducks per pond in regions where pond densities were high, indicating that mechanisms influencing local habitat use were, in part, mediated by processes occurring at larger spatial scales. Although models explained small amounts of variation of duck abundance on a per pond basis, these models explained more variation when results were aggregated to the level of survey segment, indicating reasonable performance of models for estimating duck abundance over specific areas with known pond areas. …”
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