Trend analysis of the coupling effect between new urbanization and resources-environment in the Guangdong-Hong Kong-Macao Greater Bay area by Hongyi Dou, Guoqin Zhang

“…This study constructs an urbanization-resource-environment system dynamics (SD) model that highlights the new urbanization characteristics to simulate and predict the development trends from 1990 to 2035 at urban cluster and city scales. …”
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Lower ambulatory care availability and greater hospital capacity are associated with higher hospital case volumes by Doreen Müller, Manas K. Akmatov, Dominik Graf von Stillfried

“…Global Moran’s I confirmed spatial clustering, and GWR revealed heterogeneous effects of primary-care access on hospital admissions, whereas bed capacity uniformly increased hospital cases and shorter GP distances consistently predicted more office visits across Germany. …”
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Data Mining Between Classical and Modern Applications: A Review by Ammar Yaseen Abd Alazeez

“…In reality, DM is the path toward finding associations or models among numerous fields in enormous social datasets. …”
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An Autism Spectrum Disorder Identification Method Based on 3D-CNN and Segmented Temporal Decision Network by Zhiling Liu, Ye Chen, Xinrui Dong, Jing Liu

“…The concatenated spatiotemporal features are classified using Gradient Boosting Decision Trees (GBDTs), and finally, a voting mechanism is applied to determine whether the subject belongs to the ASD group based on the prediction results. This approach not only enhances the efficiency of spatiotemporal feature extraction but also improves the model’s ability to learn complex brain activity patterns. (3) Results: The proposed method was evaluated on the ABIDE dataset, which includes 1035 subjects from 17 different brain imaging centers. …”
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Evidence for a Fast Soft X-Ray Wind in M82 from XMM-RGS by Erin Boettcher, Edmund Hodges-Kluck

“…Starburst wind models predict that metals and energy are primarily carried out of the disk by hot gas ( T > 10 ^6 K), but the low energy resolution of X-ray CCD observations results in large uncertainties on the mass and energy loading. …”
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Differentiating Cheatgrass and Medusahead Phenological Characteristics in Western United States Rangelands by Trenton D. Benedict, Stephen P. Boyte, Devendra Dahal

“…These control strategies require accurate maps of the timing and spatial patterns of the developmental stages to apply mitigation strategies in the correct areas at the right time. …”
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Application of Ensemble Kalman Filter With Covariance Localization in Data Assimilation of Radiation Belt Electrons by Yuan Lei, Xing Cao, Binbin Ni, Taorong Luo, Xiaoyu Wang

“…Abstract Data assimilation aims to enhance the system state estimate by merging sparse and diverse measurements with physical models, while considering their individual uncertainties. …”
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LWSARDet: A Lightweight SAR Small Ship Target Detection Network Based on a Position–Morphology Matching Mechanism by Yuliang Zhao, Yang Du, Qiutong Wang, Changhe Li, Yan Miao, Tengfei Wang, Xiangyu Song

“…Furthermore, we propose a Position–Morphology Matching IoU loss function, P-MIoU, which integrates center distance constraints and morphological penalty mechanisms to more precisely capture the spatial and structural differences between predicted and ground truth bounding boxes. …”
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Infra-3DRC-FusionNet: Deep Fusion of Roadside Mounted RGB Mono Camera and Three-Dimensional Automotive Radar for Traffic User Detection by Shiva Agrawal, Savankumar Bhanderi, Gordon Elger

“…These anchors guide the prediction of 2D bounding boxes, object categories, and confidence scores. …”
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Physics‐Informed Neural Networks Trained With Time‐Lapse Geo‐Electrical Tomograms to Estimate Water Saturation, Permeability and Petrophysical Relations at Heterogeneous Soils... by C. Sakar, N. Schwartz, Z. Moreno

“…Synthetic ERT surveys with electrode spacing 10 times larger than the numerical model resolution were conducted to provide 2D electrical tomograms. …”
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Enhancing seabed sediment classification with multibeam echo-sounding and self-training: a case study from the East Sea of South Korea by Changhoon Lee, Sujung Park, Daeung Yoon, Bo-Yeon Yi, Moonsoo Lim

“…To mitigate sample scarcity and class imbalance, a semi-supervised self-training loop iteratively added high-confidence pseudo-labels to the training set.ResultsField validation in the East Sea (Republic of Korea) showed that the Extreme Gradient Boosting model achieved the highest accuracy. Overall prediction accuracy increased from 60.81 % with the baseline workflow to 72.73 % after applying data interpolation, enhanced feature extraction, and self-training.DiscussionThe proposed combination of U-Net interpolation, multi-scale texture features, and semi-supervised learning significantly improves sediment classification where MBES data are incomplete and sediment samples are sparse. …”
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A long neglected world malaria map: Plasmodium vivax endemicity in 2010. by Peter W Gething, Iqbal R F Elyazar, Catherine L Moyes, David L Smith, Katherine E Battle, Carlos A Guerra, Anand P Patil, Andrew J Tatem, Rosalind E Howes, Monica F Myers, Dylan B George, Peter Horby, Heiman F L Wertheim, Ric N Price, Ivo Müeller, J Kevin Baird, Simon I Hay

“…The model incorporated data on Duffy negative phenotype frequency to suppress endemicity predictions, particularly in Africa. …”
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AUTOMATIC PALM COUNTING DENGAN METODE TEMPLATE MATCHING (STUDI KASUS DI UNIVERSITAS SAMUDRA) by Agusman, Iswahyudi, Iwan saputra

“…The use of this technology is expected to be a model that can be implemented in various other educational institutions.…”
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Review of Recent Advances in Remote Sensing and Machine Learning Methods for Lake Water Quality Management by Ying Deng, Yue Zhang, Daiwei Pan, Simon X. Yang, Bahram Gharabaghi

“…This review also discusses the effectiveness of these models in predicting various water quality parameters, offering insights into the most appropriate model–satellite combinations for different monitoring scenarios. …”
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Depth Perception Based on the Interaction of Binocular Disparity and Motion Parallax Cues in Three-Dimensional Space by Shuai Li, Shufang He, Yuanrui Dong, Caihong Dai, Jinyuan Liu, Yanfei Wang, Hiroaki Shigemasu

“…In the future, it is necessary to explore methods for easier manipulating of depth cue signals in stereoscopic images and adopting deep learning-related methods to construct models and predict depths, to meet the increasing demand of human–computer interaction in complex 3D scenarios.…”
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Estimating Wheat Chlorophyll Content Using a Multi-Source Deep Feature Neural Network by Jun Li, Yali Sheng, Weiqiang Wang, Jikai Liu, Xinwei Li

“…Compared to the second-best model, the DNN (R² = 0.799, RMSE = 6.479, RRMSE = 18.23%), the MDFNN achieved a 6.4% increase in R², and 13.5% reductions in both RMSE and RRMSE. (3) The MDFNN exhibited strong robustness and adaptability across varying years, wheat varieties, and nitrogen application levels. …”
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Use of Vision Transformer to Classify Sea Surface Phenomena in SAR Imagery by Junfei Xia, Roland Romeiser, Wei Zhang, Tamay Ozgokmen

“…In addition, our study is the first to apply a pretrained ViT model to a dataset with different polarizations and spatial resolutions—the AI4Arctic Sea Ice Challenge dataset—to rigorously assess model adaptability. …”
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Simulation and Spatio-Temporal Analysis of Soil Erosion in the Source Region of the Yellow River Using Machine Learning Method by Jinxi Su, Rong Tang, Huilong Lin

“…Given these challenges, the objectives of this study were to develop a suitable assessment and prediction model for soil erosion tailored to the SRYR’s needs. …”
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Microscopic pore combination type identification of tight sandstone reservoir based on improved swin transformer architecture by Zhenyu Pang, Zhicong Chen, Sijie Lu, Zhenbo Cai, Yuqing Lu, Mengting Peng

“…Experimental results demonstrate that SwinLSC achieves a prediction accuracy of 93.3 %, significantly outperforming the comparative models. …”
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Delimitation of Landslide Areas in Optical Remote Sensing Images Across Regions via Deep Transfer Learning by Zan Wang, Shengwen Qi, Yu Han, Bowen Zheng, Yu Zou, Yue Yang

“…A post-processing module is integrated into the Mask R-CNN architecture to address the challenge of overlapping mask predictions for individual landslide objects. The results indicate that the Mask R-CNN model exhibits superior overall performance in comparison with the U-Net model and is more suitable for tasks requiring detailed delineation of the object outlines in images. …”
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