Integrated PSInSAR and SBAS-InSAR Analysis for Landslide Detection and Monitoring DOI Creative Commons
Sajid Hussain, Bin Pan,

Wajid Hussain

и другие.

Physics and Chemistry of the Earth Parts A/B/C, Год журнала: 2025, Номер 139, С. 103956 - 103956

Опубликована: Апрель 30, 2025

Язык: Английский

Landslide susceptibility assessment of the Wanzhou district: Merging landslide susceptibility modelling (LSM) with InSAR-derived ground deformation map DOI Creative Commons
Chao Zhou, Lulu Gan, Ying Cao

и другие.

International Journal of Applied Earth Observation and Geoinformation, Год журнала: 2025, Номер 136, С. 104365 - 104365

Опубликована: Янв. 18, 2025

Язык: Английский

Процитировано

1

COPD: Pneumonia and Pneumothorax Detection in Chest X-rays: Vision Transfer based on Deep Learning DOI
Yousef S Aldabayan

Research Square (Research Square), Год журнала: 2025, Номер unknown

Опубликована: Апрель 17, 2025

Abstract Recently, there has been a growing interest in the application of deep learning for automated analysis chest X-rays (CXRs) especially detection pneumothorax. Conventional models such as convolutional neural networks (CNNs) have shown to perform well CXR classification. However, CNN-based methods are limited by local dependence feature extraction their inherent form, which prevents them from capturing long-range dependencies medical images. Furthermore, CNNs domain shift problem, limits flexibility varying imaging conditions and typically black box that difficult interpret incorporate into clinical decision-making processes. In this paper, we designed novel Vision Transfer (ViT) based framework classification pneumonia pneumothorax CXRs. contrast CNNs, ViTs use self-attention model global thus well-placed detect diffuse opacities pneumonia, pleural abnormalities The ViT was fine-tuned on dataset CXRs, were labelled with advanced preprocessing augmentation data better generalization. To improve interpretability, used maps develop more transparent explainable AI diagnostic system. our approach, showed had superior performance high sensitivity specificity across both conditions. provided intrinsic interpretability highlighting clinically relevant regions X-rays, aligned expert radiological assessments. also generalization datasets, reducing biases typical CNN architectures. results show can be potential new approach field imaging, interpretation X-rays. Through enhancing accuracy, guaranteeing generalization, ensuring AI-assisted diagnostics radiology workflows facilitate fast accurate decision making respiratory disease detection. Future work will include exploring multi-modal fusion approaches real-world validation further enhance effectiveness transformer-based healthcare practice.

Язык: Английский

Процитировано

0

Eutrophication Exacerbates Microplastic Bioaccumulation Risks in Coastal Fish DOI

Chunhui Liu,

Xiangang Hu,

Can Shen

и другие.

Environmental Science & Technology, Год журнала: 2025, Номер unknown

Опубликована: Апрель 22, 2025

Microplastic bioaccumulation (MPB) within marine fish through the food chain has been extensively validated in traditional experimental studies. However, idealized conditions of these studies fail to fully capture complex, nonlinear interactions among microplastics, biota, and environmental factors real-world scenarios, spatiotemporal characteristics risks are lacking studies, hindering accurate assessment control risk. To address above knowledge gaps, we constructed an improved sparrow search algorithm/geographic random forest (ISSA-GRF) conceptual framework analyzed MPB 82 common species from coastal ecosystems. The rate 22 major ecosystems increased by average 3.56% over past decade. Hotspot areas such as Red Sea coast, Gulf Thailand Sulawesi coast were identified; rates 8.00%, 5.68%, 5.34%, respectively. Ocean eutrophication, triggered changes nutrient levels, was revealed main driver via a causal analysis. These findings not only independently validate increasing risk microplastic biological accumulation outside laboratory environment but also highlight importance controlling eutrophication mitigate associated with microplastics.

Язык: Английский

Процитировано

0

Displacement prediction and failure mechanism analysis of rainfall-induced colluvial landslides DOI
Yabo Li,

Xinli Hu,

Haiyan Zhang

и другие.

Journal of Hydrology, Год журнала: 2025, Номер unknown, С. 133361 - 133361

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0

Integrated PSInSAR and SBAS-InSAR Analysis for Landslide Detection and Monitoring DOI Creative Commons
Sajid Hussain, Bin Pan,

Wajid Hussain

и другие.

Physics and Chemistry of the Earth Parts A/B/C, Год журнала: 2025, Номер 139, С. 103956 - 103956

Опубликована: Апрель 30, 2025

Язык: Английский

Процитировано

0