Mesoscopic characterization and modeling of microcracking in cementitious materials by the extended finite element method

AbstractThis study develops a mesoscopic framework and methodology for the modeling of microcracks in concrete. A new algorithm is first proposed for the generation of random concrete meso-structure including microcracks and then coupled with the extended finite element method to simulate the heterogeneities and discontinuities present in the meso-structure of concrete. The proposed procedure is verified and exemplified by a series of numerical simulations. The simulation results show that microcracks can exert considerable impact on the fracture performance of concrete. More broadly, this work provides valuable insight into the initiation and propagation mechanism of microcracks in concrete and helps to foster a better understanding of the micro-mechanical behavior of cementitious materials

3D Shape Augmentation with Content-Aware Shape Resizing

Recent advancements in deep learning for 3D models have propelled breakthroughs in generation, detection, and scene understanding. However, the effectiveness of these algorithms hinges on large training datasets. We address the challenge by introducing Efficient 3D Seam Carving (E3SC), a novel 3D model augmentation method based on seam carving, which progressively deforms only part of the input model while ensuring the overall semantics are unchanged. Experiments show that our approach is capable of producing diverse and high-quality augmented 3D shapes across various types and styles of input models, achieving considerable improvements over previous methods. Quantitative evaluations demonstrate that our method effectively enhances the novelty and quality of shapes generated by other subsequent 3D generation algorithms

Learning Robust Medical Image Segmentation from Multi-source Annotations

Collecting annotations from multiple independent sources could mitigate the impact of potential noises and biases from a single source, which is a common practice in medical image segmentation. Learning segmentation networks from multi-source annotations remains a challenge due to the uncertainties brought by the variance of annotations and the quality of images. In this paper, we propose an Uncertainty-guided Multi-source Annotation Network (UMA-Net), which guides the training process by uncertainty estimation at both the pixel and the image levels. First, we developed the annotation uncertainty estimation module (AUEM) to learn the pixel-wise uncertainty of each annotation, which then guided the network to learn from reliable pixels by weighted segmentation loss. Second, a quality assessment module (QAM) was proposed to assess the image-level quality of the input samples based on the former assessed annotation uncertainties. Importantly, we introduced an auxiliary predictor to learn from the low-quality samples instead of discarding them, which ensured the preservation of their representation knowledge in the backbone without directly accumulating errors within the primary predictor. Extensive experiments demonstrated the effectiveness and feasibility of our proposed UMA-Net on various datasets, including 2D chest X-ray segmentation, fundus image segmentation, and 3D breast DCE-MRI segmentation

Research status and hotspots of social frailty in older adults: a bibliometric analysis from 2003 to 2022

BackgroundSocial Frailty is a significant public health concern affecting the elderly, particularly with the global population aging rapidly. Older adults with social frailty are at significantly higher risk of adverse outcomes such as disability, cognitive impairment, depression, and even death. In recent years, there have been more and more studies on social frailty, but no bibliometrics has been used to analyze and understand the general situation in this field. Therefore, by using CiteSpace, VOSviewer, and Bilioshiny software programs, this study aims to analyze the general situation of the research on social frailties of the older adults and determine the research trends and hot spots.MethodsA bibliometric analysis was conducted by searching relevant literature on the social frailty of the older adults from 2003 to 2022 in the Web of Science core database, using visualization software to map publication volume, country and author cooperation networks, keyword co-occurrences, and word emergence.ResultsWe analyzed 415 articles from 2003 to 2022. Brazil has the highest number of articles in the field of social frailty of the older adults, and the United States has the highest number of cooperative publications. Andrew MK, from Canada, is the most published and co-cited author, with primary research interests in geriatric assessment, epidemiology, and public health. “Social Vulnerability,” “Health,” “Frailty,” “Mortality,” and “Older Adult” are among the research hotspots in this field. “Dementia,” “Alzheimer’s disease,” “Population,” and “Covid-19” are emerging research trends in social frailty among the older adults.ConclusionThis scientometric study maps the research hotspots and trends for the past 20 years in social frailty among the older adults. Our findings will enable researchers to better understand trends in this field and find suitable directions and partners for future research

Forming a biomathematical learning alliance across traditional academic departments

Across the United States, many generalized programs have focused on retention of minority students in the sciences with varying degrees of success. Paradoxically, this challenge exists despite expanding career opportunities in industry, academia, and government for those skilled at the intersection of biology and mathematics. Here we describe a cross-departmental learning alliance (iBLEND- an Integrative Biomathematics Learning and Empowerment Network for Diversity) which directly targets these recognized challenges. Our goal is for the iBLEND project to have significant spillover effects for our university by developing new interdisciplinary collaborations that benefit our students. The iBLEND is a proactive, intensive approach in order to bridge campus chasms for both faculty and undergraduate students by positively influencing academic programs through interdisciplinary training coupled with strong evaluation and assessments. By leveraging our recent surge of competitive research activity, innovative instruction, and collaboration, the iBLEND advances our transformation to the next level by establishing a broader bridge for our undergraduates at the interface of mathematics and biology. In working together, the math and biology students learned to bridge language barriers inhibiting interdisciplinary explorations. Students were closely involved with faculty mentors in core laboratories and developed cross-disciplinary research skills that enhanced their post-graduate career opportunities. Using systems biology tools combined with targeted mathematics classroom work, students merged data from their lab bench experiments with mathematical models to determine how various changes impacted an overall organism and its functions. The students had hands-on training with a myriad of computational, simulations, data mining and data analysis tools needed in approaching their projects

Decoupled Attention Network for Text Recognition

Text recognition has attracted considerable research interests because of its various applications. The cutting-edge text recognition methods are based on attention mechanisms. However, most of attention methods usually suffer from serious alignment problem due to its recurrency alignment operation, where the alignment relies on historical decoding results. To remedy this issue, we propose a decoupled attention network (DAN), which decouples the alignment operation from using historical decoding results. DAN is an effective, flexible and robust end-to-end text recognizer, which consists of three components: 1) a feature encoder that extracts visual features from the input image; 2) a convolutional alignment module that performs the alignment operation based on visual features from the encoder; and 3) a decoupled text decoder that makes final prediction by jointly using the feature map and attention maps. Experimental results show that DAN achieves state-of-the-art performance on multiple text recognition tasks, including offline handwritten text recognition and regular/irregular scene text recognition.Comment: 9 pages, 8 figures, 6 tables, accepted by AAAI-202