Higher hemoglobin levels are associated with impaired left ventricular global strains in metabolic syndrome: a 3.0 T CMR feature tracking study by Xue Li, Shi-Qin Yu, Zhi-Gang Yang, Bi-Yue Hu, Ke Shi, Jing Wang, Xue-Ming Li, Ge Zhang, Wen-Rong Li, Rong Xu, Yuan Li

“…Targeted monitoring and management of higher Hb levels in MetS patients may help mitigate further deterioration of cardiac function.…”
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Unlocking the potential of digital pathology: Novel baselines for compression by Maximilian Fischer, Peter Neher, Peter Schüffler, Sebastian Ziegler, Shuhan Xiao, Robin Peretzke, David Clunie, Constantin Ulrich, Michael Baumgartner, Alexander Muckenhuber, Silvia Dias Almeida, Michael Gőtz, Jens Kleesiek, Marco Nolden, Rickmer Braren, Klaus Maier-Hein

“…Our metric allows for a general and standardized evaluation of lossy compression schemes and mitigates the requirement to independently assess different downstream tasks. …”
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Estimation of soil properties using Hyperspectral imaging and Machine learning by Eirini Chlouveraki, Nikolaos Katsenios, Aspasia Efthimiadou, Erato Lazarou, Kalliopi Kounani, Eleni Papakonstantinou, Dimitrios Vlachakis, Aikaterini Kasimati, Ioannis Zafeiriou, Borja Espejo-Garcia, Spyros Fountas

“…These steps aim to mitigate redundancy and noise, enhancing the model's generalization. …”
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Optimizing green infrastructure strategies for microclimate regulation and air quality improvement in urban environments: A case study by Soniya Kaveh, Amin Habibi, Mohammad Nikkar, Ardalan Aflaki

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Subtidal seagrass and blue carbon mapping at the regional scale: a cloud-native multi-temporal Earth Observation approach by Mar Roca, Chengfa Benjamin Lee, Avi Putri Pertiwi, Alina Blume, Isabel Caballero, Gabriel Navarro, Dimosthenis Traganos

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Combining sodium-glucose co-transporter-2 inhibitor with mesenchymal stem cells and brown adipose tissue (BAT) and white adipose tissue (WAT) transplantation to mitigate the progre... by Stephany Beyerstedt, Marcella L. Franco, Alanah K. G. Carlos, Jaqueline Arjona, Gleice R. Josefi-Rocha, Bruno S. Barbosa, Maria Theresa A. Balby-Rocha, Andrei Furlan da Silva, Tuany Marques Reiter Alves, Melise Oliveira Mariano, Maria Clara Soares Klein, Érika Bevilaqua Rangel

“…While conventional treatments show potential in mitigating DKD progression, a combination of strategies is required to both impede its development and repair damaged structures. …”
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Kolmogorov–Arnold–Enhanced Nonlinear Expansions for Fine-Grained Feature Amplification in Robust Near-Shore SAR Vessel Discrimination by Hankang Wang, Gaopeng Huang, Zhao Huang, Xingru Huang, Zhaoyang Xu, Zhiwen Zheng, Shanwei Liu, Shiqing Wei, Jin Liu, Xiaoshuai Zhang

“…This structure preserves small-target details by propagating enriched semantic information across detection layers, mitigating feature loss during downsampling. Finally, a Detail Enhancement Detection Head is proposed to reduce computational overhead through shared convolutional layers, while enhancing local feature utilization via direction-sensitive convolutions and group normalization. …”
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Designing digital conversational agents for youth with multiple mental health conditions: Insights on key features from a youth-engaged qualitative descriptive study by Jingyi Hou, Jamie Gibson, Thalia Phi, Brian Ritchie, Louise Gallagher, Gillian Strudwick, George Foussias, Darren B Courtney, Aristotle Voineskos, Stephanie Ameis, Kristin Cleverley, Lisa D Hawke

“…Results Four key themes were generated from the focus group data: (1) the importance of a customizable and flexible design for personalization; (2) confidentiality, privacy features and risk mitigation features; (3) the need for reliable, informative content that is user tested and validated; (4) a friendly and human-like interaction style. …”
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Small Object Detection in UAV Remote Sensing Images Based on Intra-Group Multi-Scale Fusion Attention and Adaptive Weighted Feature Fusion Mechanism by Zhe Yuan, Jianglei Gong, Baolong Guo, Chao Wang, Nannan Liao, Jiawei Song, Qiming Wu

“…Such modifications enhance the model’s ability to capture the minute features of small objects. In addition, an adaptive feature fusion module is introduced, which is capable of automatically adjusting the weights based on the significance and contribution of features at different scales to improve the detection sensitivity for small objects. …”
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Landslide early warning based on improved tangential angle and displacement rate: A case study of the Leijiashan landslide in Shimen County, Hunan Province by Quanming CHEN, Weimin HUANG, Jiao LI

“…By utilizing the GNSS system，complete displacement-time curves are obtained, and the types and features of these curves are analyzed to identify the evolutionary characteristics of the landslide，which include three stages: initial deformation, constant deformation, and accelerated deformation. …”
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Topic adversarial neural network for cross-topic cyberbullying detection by Shufeng Xiong, Wenzhuo Liu, Bingkun Wang, Yinchao Che, Lei Shi

“…TANN integrates a multi-level feature extractor with a topic discriminator and a cyberbullying detector. …”
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Soil Moisture Prediction Using Remote Sensing and Machine Learning Algorithms: A Review on Progress, Challenges, and Opportunities by Manoj Lamichhane, Sushant Mehan, Kyle R. Mankin

“…We reviewed the literature to extract and synthesize ML algorithms, reliable input features, and challenges in SM estimation using RS data. …”
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SwinNowcast: A Swin Transformer-Based Model for Radar-Based Precipitation Nowcasting by Zhuang Li, Zhenyu Lu, Yizhe Li, Xuan Liu

“…Through the novel design of a multi-scale feature balancing module (M-FBM), the model dynamically integrates local-scale features with global spatiotemporal dependencies. …”
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DEDSWIN-Net: Dual Encoder Dilated Convolution and Swin Transformer Network for the Classification of Liver CT Images by Jyoshna Allenki, Hemant Kumar Soni

“…To resolve this concern, this paper suggests a novel dual encoder deep learning structure named DEDSWIN-Net to mitigate this problem. The proposed framework consists of four components: a dilated convolution-based encoder, a transformer-based encoder, a multi-scale multiple feature fusion decoder (MSMFD), and a DL training model. …”
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SE-ResUNet Using Feature Combinations: A Deep Learning Framework for Accurate Mountainous Cropland Extraction Using Multi-Source Remote Sensing Data by Ling Xiao, Jiasheng Wang, Kun Yang, Hui Zhou, Qianwen Meng, Yue He, Siyi Shen

“…The results showed the following: (1) feature fusion (NDVI + TerrainIndex + SAR) had the best performance (OA: 97.11%; F1-score: 96.41%; IoU: 93.06%), significantly reducing shadow/cloud interference. (2) SE-ResUNet outperformed ResUNet by 3.53% for OA and 8.09% for IoU, emphasizing its ability to recalibrate channel-wise features and refine edge details. (3) The model exhibited robustness across diverse slopes/aspects (OA > 93.5%), mitigating terrain-induced misclassifications. …”
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MMS-EF: A Multi-Scale Modular Extraction Framework for Enhancing Deep Learning Models in Remote Sensing by Hang Yu, Weidong Song, Bing Zhang, Hongbo Zhu, Jiguang Dai, Jichao Zhang

“…The results demonstrate that the proposed approach effectively mitigates the limitations of traditional preprocessing methods, significantly improving feature extraction accuracy and exhibiting strong adaptability across different datasets.…”
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Surface urban heat island distribution and socioeconomic inequalities in a major Latin American agglomeration by Jorge Méndez-Astudillo, Ernesto Caetano

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Neural-XGBoost: A Hybrid Approach for Disaster Prediction and Management Using Machine Learning by Muhammad Asim Saleem, Ashir Javeed, Watit Benjapolakul, Wattanasak Srisiri, Surachai Chaitusaney, Pasu Kaewplung

“…Effective disaster prediction is essential for disaster management and mitigation. This study addresses a multi-classification problem and proposes the Neural-XGBoost disaster prediction model (N-XGB), a hybrid model that combines neural networks (NN) for feature extraction with XGBoost for classification. …”
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Production and Analysis of a Landslide Susceptibility Map Covering Entire China by Guo Zhang, Yutao Liu, Zhenwei Chen, Zixing Xu, Yuan Yuan, Shunyao Wang, Weiqi Lian, Hang Xu, Zan Ding, Run Wang

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Enhancing Neural Network Interpretability Through Deep Prior-Guided Expected Gradients by Su-Ying Guo, Xiu-Jun Gong

“…It resolves the baseline misalignment by initiating gradient path integration from learned prior baselines, which are derived from the deep features of CNN layers. This approach not only mitigates feature absence artifacts but also amplifies critical feature contributions through adaptive gradient aggregation. …”
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