Review of flow instability in gas-turbine compression system by Jingyi Zeng, Feng Liu

“…Future directions are proposed, emphasizing the development of unified theoretical models, accelerated numerical methods, and full-annulus experiments to enhance stall prediction and active control strategies.…”
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Past influences present: Mammalian species from different biogeographic pools sort environmentally in the Indian subcontinent by Krishnapriya Tamma, Aniruddha Marathe, Uma Ramakrishnan

“…Additionally, biogeographic groups sorted along environmental space, in keeping with our predictions based on their global distributions. Finally, analyses across mammalian orders had low predictive value, suggesting that shared phylogenetic history is relatively less important than biogeographic ancestry in determining relationships to environment. …”
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Optimizing county-level infectious respiratory disease forecasts: a pandemic case study integrating social media-based physical and social connectivity networks by Fengrui Jing, Zhenlong Li, Shan Qiao, M. Naser Lessani, Huan Ning, Wenjun Ma, Jinjing Hu, Pan Yang, Xiaoming Li

“…However, existing time series forecasting models that incorporate human mobility data have faced challenges in making localized predictions on a large scale across the country due to data costs and constraints. …”
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Hybrid Attention and Multiscale Module for Alzheimer's Disease Classification by WANG Yuanjun

“…Additionally, it integrates multiscale spatial scale features of Alzheimer's disease by using a multiscale information fusion module based on dilated convolution and soft attention, enhancing early diagnosis and prediction. …”
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Identification of recurrent dynamics in distributed neural populations. by Rodrigo Osuna-Orozco, Edward Castillo, Kameron Decker Harris, Samantha R Santacruz

“…Large-scale recordings of neural activity over broad anatomical areas with high spatial and temporal resolution are increasingly common in modern experimental neuroscience. …”
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How does high temperature weather affect tourists' nature landscape perception and emotions? A machine learning analysis of Wuyishan City, China. by Cuicui Ye, Zhengyan Chen, Zheng Ding

“…The results indicate: (1) Emotion prediction and spatial analysis reveal a significant increase in the proportion of negative emotions under high-temperature conditions, reaching 30.1%, with negative emotion hotspots concentrated in the downtown area, whereas, under non-high temperature conditions, negative emotions accounted for 14.1%, with a more uniform spatial distribution. (2) Under non-high temperature conditions, the four most influential factors on tourists' emotions were Color complexity (0.73), Visual entropy (0.71), Greenness (0.68), and Aquatic rate (0.6). …”
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A novel framework for multi-layer soil moisture estimation with high spatio-temporal resolution based on data fusion and automated machine learning by Shenglin Li, Yang Han, Caixia Li, Jinglei Wang

“…Initially, we generated seamless 30 m resolution metrics, including the normalized difference vegetation index (NDVI), land surface temperature (LST), and surface albedo, by employing the modified neighborhood similar pixel interpolator (MNSPI) in conjunction with the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM). …”
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Contribution of Moisture Recycling to Water Availability in China by Qiang An, Liu Liu, Lixin Wang, Kun Yang, Yongming Cheng, Jing Liu, Guanhua Huang

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Design and control algorithm of a motion sensing-based fruit harvesting robot by Ziwen CHEN, Yuhang CHEN, Hui LI, Pei WANG

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Improving Sub-Industry GDP Estimation With SDGSAT-1 Multispectral Nighttime Light and Thermal Infrared Data: Effectiveness and Potential by Lingxian Zhang, Zuoqi Chen, Wenkang Gong, Congxiao Wang, Jing Xiong, Linxin Dong, Jingwen Ni, Yan Huang, Bailang Yu

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Density, Climate, and Stochasticity Shape Four Centuries of Population Dynamics for Two Long‐Lived Tree Species by Ellen Waddle, Mark R. Lesser, Christopher Steenbock, Daniel F. Doak

“…Indeed, exclusion of any one of these effects greatly reduced predictive power of our population growth models. Given the similarity in the abiotic characteristics of these sites, the varying importance of these classes of effects was surprising but speaks to the need to consider multiple effects when predicting the dynamics of small and colonizing populations.…”
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Integration of multimodal imaging data with machine learning for improved diagnosis and prognosis in neuroimaging by Saurabh Bhattacharya, Sashikanta Prusty, Sanjay P. Pande, Monali Gulhane, Santosh H. Lavate, Nitin Rakesh, Saravanan Veerasamy

“…This work introduces a novel hybrid deep learning method combining CNN, GRU, and a Dynamic Cross-Modality Attention Module to help more efficiently blend spatial and temporal brain data. Through working around issues with current multimodal fusion techniques, our approach increases the accuracy and readability of diagnoses.MethodsUtilizing CNNs and models of temporal dynamics from fMRI connection measures utilizing GRUs, the proposed approach extracts spatial characteristics from sMRI. …”
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Machine learning for the rElapse risk eValuation in acute biliary pancreatitis: The deep learning MINERVA study protocol by Mauro Podda, Adolfo Pisanu, Gianluca Pellino, Adriano De Simone, Lucio Selvaggi, Valentina Murzi, Eleonora Locci, Matteo Rottoli, Giacomo Calini, Stefano Cardelli, Fausto Catena, Carlo Vallicelli, Raffaele Bova, Gabriele Vigutto, Fabrizio D’Acapito, Giorgio Ercolani, Leonardo Solaini, Alan Biloslavo, Paola Germani, Camilla Colutta, Savino Occhionorelli, Domenico Lacavalla, Maria Grazia Sibilla, Stefano Olmi, Matteo Uccelli, Alberto Oldani, Alessio Giordano, Tommaso Guagni, Davina Perini, Francesco Pata, Bruno Nardo, Daniele Paglione, Giusi Franco, Matteo Donadon, Marcello Di Martino, Dario Bruzzese, Daniela Pacella

“…The model includes the following steps: the spatial transformation of variables using kernel Principal Component Analysis (kPCA), the creation of 2D images from transformed data, the application of convolutional filters, max-pooling, flattening, and final risk prediction via a fully connected layer. …”
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Landslide Susceptibility Level Mapping in Kozhikode, Kerala, Using Machine Learning-Based Random Forest, Remote Sensing, and GIS Techniques by Pradeep Kumar Badapalli, Anusha Boya Nakkala, Raghu Babu Kottala, Sakram Gugulothu, Fahdah Falah Ben Hasher, Varun Narayan Mishra, Mohamed Zhran

“…The RF model was trained and validated using a 50:50 split of landslide and non-landslide points, with variable importance values derived to weight each predictive factor of the raster layer in ArcGIS. …”
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Multi-Modal Social Media Analysis via SHAP-Based Explanation: A Framework for Public Art Perception by Tianhao Guo, Zhengyang Lu, Feng Wang

“…We propose a multi-modal framework integrating visual and linguistic analysis: visual content undergoes ResNet-152 feature extraction and Qwen-7B-Chat captioning, while textual data is processed through hierarchical topic modeling and sentiment classification. Through analysis of 103,427 geo-tagged Weibo posts across thirteen administrative regions, this study employs fine-tuned Qwen-7B-Chat architecture integrated with spatial random forest modeling to decode urban-rural variations in artistic appreciation. …”
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Framework for timely perception of spatiotemporal crowd congestion risk in public spaces based on video pedestrian tracking and geographic mapping by Shaojun Liu, Ling Zhang, Junlian Ge, Weitao Li, Yi Long

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Using the Remote Sensing Method to Simulate the Land Change in the Year 2030 by Burcu Degerli, Mehmet Çetin

“…The predictive values of the LU/LC change that will occur in 2030, calculated with the MLP‑ANN model based on Machine Learning algorithms and mapped with the QGIS 3.16 program. …”
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GD-Det: Low-Data Object Detection in Foggy Scenarios for Unmanned Aerial Vehicle Imagery Using Re-Parameterization and Cross-Scale Gather-and-Distribute Mechanisms by Rui Shi, Lili Zhang, Gaoxu Wang, Shutong Jia, Ning Zhang, Chensu Wang

“…The model is primarily composed of three components: (i) A lightweight re-parameterization feature extraction module which integrates RepVGG blocks into multi-concat blocks to enhance the model’s spatial perception and feature diversity during training. …”
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Detection of Banana Diseases Based on Landsat-8 Data and Machine Learning by Renata Retkute, Kathleen S. Crew, John E. Thomas, Christopher A. Gilligan

“…We used a pixel-level random forest (RF) model to predict 11 key vegetation indices (VIs) as a function of historical meteorological conditions, specifically daytime and nighttime temperature from MODIS and precipitation from NASA GES DISC. …”
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Plot‐level satellite imagery can substitute for UAVs in assessing maize phenotypes across multistate field trials by Nikee Shrestha, Anirudha Powadi, Jensina Davis, Timilehin T. Ayanlade, Huyu Liu, Michael C. Tross, Ramesh K. Mathivanan, Jordan Bares, Lina Lopez‐Corona, Jonathan Turkus, Lisa Coffey, Talukder Zaki Jubery, Yufeng Ge, Soumik Sarkar, James C. Schnable, Baskar Ganapathysubramanian, Patrick S. Schnable

“…This dataset and benchmarks have the potential to enable predictive models that could guide farmers and crop breeders in decision‐making. …”
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