Large-scale flood modeling and forecasting with FloodCast.

Large-scale hydrodynamic models generally rely on fixed-resolution spatial grids and model parameters as well as incurring a high computational cost. This limits their ability to accurately forecast flood crests and issue time-critical hazard warnings. In this work, we build a fast, stable, accurate, resolution-invariant, and geometry-adaptive flood modeling and forecasting framework that can perform at large scales, namely FloodCast.

A deep learning fusion network trained with clinical and high-frequency ultrasound images in the multi-classification of skin diseases in comparison with dermatologists: a prospective and multicenter study.

Background: Clinical appearance and high-frequency ultrasound (HFUS) are indispensable for diagnosing skin diseases by providing internal and external information. However, their complex combination brings challenges for primary care physicians and dermatologists. Thus, we developed a deep multimodal fusion network (DMFN) model combining analysis of clinical close-up and HFUS images for binary and multiclass classification in skin diseases.

Hybrid Quantum-Classical Convolutional Neural Network Model for Image Classification.

Image classification plays an important role in remote sensing. Earth observation (EO) has inevitably arrived in the big data era, but the high requirement on computation power has already become a bottleneck for analyzing large amounts of remote sensing data with sophisticated machine learning models. Exploiting quantum computing might contribute to a solution to tackle this challenge by leveraging quantum properties.

Three-dimensional convolutional neural network model to identify clinically significant prostate cancer in transrectal ultrasound videos: a prospective, multi-institutional, diagnostic study.

Background: Identifying patients with clinically significant prostate cancer (csPCa) before biopsy helps reduce unnecessary biopsies and improve patient prognosis. The diagnostic performance of traditional transrectal ultrasound (TRUS) for csPCa is relatively limited. This study was aimed to develop a high-performance convolutional neural network (CNN) model (P-Net) based on a TRUS video of the entire prostate and investigate its efficacy in identifying csPCa.

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images.

In recent years, the incidence of thyroid cancer has been increasing. Thyroid nodule detection is critical for both the detection and treatment of thyroid cancer. Convolutional neural networks (CNNs) have achieved good results in thyroid ultrasound image analysis tasks.

Chronic social defeat alters behaviors and neuronal activation in the brain of female Mongolian gerbils.

Chronic social defeat has been found to be stressful and to affect many aspects of the brain and behaviors in males. However, relatively little is known about its effects on females. In the present study, we examined the effects of repeated social defeat on social approach and anxiety-like behaviors as well as the neuronal activation in the brain of sexually naïve female Mongolian gerbils (Meriones unguiculatus).

Semi-supervised bidirectional alignment for Remote Sensing cross-domain scene classification.

Remote sensing (RS) image scene classification has obtained increasing attention for its broad application prospects. Conventional fully-supervised approaches usually require a large amount of manually-labeled data. As more and more RS images becoming available, how to make full use of these unlabeled data is becoming an urgent topic.

Identification of benign and malignant thyroid nodules based on dynamic AI ultrasound intelligent auxiliary diagnosis system.

Background: Dynamic artificial intelligence (AI) ultrasound intelligent auxiliary diagnosis system (Dynamic AI) is a joint application of AI technology and medical imaging data, which can perform a real-time synchronous dynamic analysis of nodules. The aim of this study is to investigate the value of dynamic AI in differentiating benign and malignant thyroid nodules and its guiding significance for treatment strategies.

Methods: The data of 607 patients with 1007 thyroid nodules who underwent surgical treatment were reviewed and analyzed, retrospectively.

The urban morphology on our planet - Global perspectives from space.

Urbanization is the second largest mega-trend right after climate change. Accurate measurements of urban morphological and demographic figures are at the core of many international endeavors to address issues of urbanization, such as the United Nations' call for "Sustainable Cities and Communities". In many countries - particularly developing countries -, however, this database does not yet exist.

Adversarial Shape Learning for Building Extraction in VHR Remote Sensing Images.

Building extraction in VHR RSIs remains a challenging task due to occlusion and boundary ambiguity problems. Although conventional convolutional neural networks (CNNs) based methods are capable of exploiting local texture and context information, they fail to capture the shape patterns of buildings, which is a necessary constraint in the human recognition. To address this issue, we propose an adversarial shape learning network (ASLNet) to model the building shape patterns that improve the accuracy of building segmentation.

Improving diagnostic accuracy for arteries of lower extremities with dual-energy spectral CT imaging.

Purpose: Investigate the clinical value of improving diagnostic accuracy for arteries of lower extremities with low energy images in dual-energy spectral CT (DEsCT) imaging.

Method: 110 (mean age, 67 ± 10 years) and 72 (mean age, 65 ± 13 years) patients underwent CT angiography (CTA) in the lower extremities using dual-energy and conventional (100kVp) imaging mode, retrospectively. The 50 keV monochromatic images were reconstructed in the DEsCT group for analysis.

Building segmentation through a gated graph convolutional neural network with deep structured feature embedding.

Automatic building extraction from optical imagery remains a challenge due to, for example, the complexity of building shapes. Semantic segmentation is an efficient approach for this task. The latest development in deep convolutional neural networks (DCNNs) has made accurate pixel-level classification tasks possible.

Life cycle cost and environmental assessment for resource-oriented toilet systems.

The rich content of nutrients in human waste provides an outlook for turning it from pollutants to potential resources. The pilot-scale resource-oriented toilet with forward osmosis technology was demonstrated to have advantages to recover clean water, nitrogen, phosphorus, potassium, biogas, and heat from urine and feces. For the possibility of further full-scale implementation in different scenarios, six resource-oriented toilet systems and one conventional toilet system were designed in this study.

Analysis of intermittency in under-resolved smoothed-particle-hydrodynamics direct numerical simulations of forced compressible turbulence.

We perform three-dimensional under-resolved direct numerical simulations of forced compressible turbulence using the smoothed particle hydrodynamics (SPH) method and investigate the Lagrangian intermittency of the resulting hydrodynamic fields. The analysis presented here is motivated by the presence of typical stretched tails in the probability density function (PDF) of the particle accelerations previously observed in two-dimensional SPH simulations of uniform shear flow [Ellero et al., Phys.