Test research on seismic performance of story-adding frame structure

U radu se opisuju usporedni eksperimenti između metode za dodatnu konstrukciju kata od armiranog betona i one od lakog čelika. Ispitivanje kvazi-statičkog testa te usporedba i analiza eksperimentalnih rezultata također su provedeni i za okvir dodatne konstrukcije od armiranog betona kao i za onaj od lakog čelika pri istim radnim uvjetima. Rezultati su pokazali da iako obije konstrukcije okvira donekle zadovoljavaju seizmičke zahtjeve, postoje i jasne prednosti i nedostaci u obije metode. Rezultati dalje ukazuju i primjenjivost i relevantne probleme na koje je potrebno usmjeriti pozornost za obije metode dodatka kata.This paper conducts comparative experiments between reinforced concrete story-adding structure and the lightweight steel story-adding method. Quasi-static test research and a comparison and analysis of the experimental results are also performed for both reinforced concrete story-adding framework and light steel story-adding under the same working conditions. The results showed that although both structural frameworks do meet seismic construction requirements to some degree, the results of experimentation reflect distinct advantages and disadvantages for both methods. Results further indicate the applicability and relevant problems requiring attention for both story-adding methods

Research on Liquefaction Resistance of Bucket Foundation for Offshore Wind Turbines

[Introduction] With the increasing demand for clean energy, the offshore wind power sector has seen a spurt of progress in recent years, and the bucket foundation has become the preferred choice for offshore wind turbines considering its good economy, convenient construction, and recyclability. Due to the widespread distribution of seismic zones in China, the seismic performance of bucket foundation is a crucial consideration for structural design. The bucket foundation is featured by high structure stiffness, so that the probability of structure damage caused by earthquake is low, and the failure under earthquake is mainly caused by the liquefaction of the foundation soil. For this purpose, the paper focuses on the seismic performance of bucket foundation in sandy soil. [Method] The liquefaction resistance of sandy soil for bucket foundation was analyzed by shaking table tests in this paper. The study objects included four types of bucket foundation in sandy soil, namely mono-bucket foundation (MBF), composite bucket foundation (CBF), three-bucket jacket foundation (TBJF) and four-bucket jacket foundation (FBJF). [Result] By carrying out shaking table tests, the excess pore pressure ratios of sandy soil for different types of bucket foundation under earthquake are obtained, and the impact mechanism of bucket foundation on the anti-liquefaction performance of sand soil is clarified. [Conclusion] The bucket foundation can improve the liquefaction resistance of sand, since the additional load effect of the superstructure and the hoop effect of bucket skirt weakens its shear shrinkage. The test results of MBF are compared with those of CBF, and the test results of TBJF are compared with those of FBJF. It is found that the seismic performance of CBF and FBJF is respectively superior to that of MBF and TBJF

LViT: Language meets Vision Transformer in Medical Image Segmentation

Deep learning has been widely used in medical image segmentation and other aspects. However, the performance of existing medical image segmentation models has been limited by the challenge of obtaining sufficient high-quality labeled data due to the prohibitive data annotation cost. To alleviate this limitation, we propose a new text-augmented medical image segmentation model LViT (Language meets Vision Transformer). In our LViT model, medical text annotation is incorporated to compensate for the quality deficiency in image data. In addition, the text information can guide to generate pseudo labels of improved quality in the semi-supervised learning. We also propose an Exponential Pseudo label Iteration mechanism (EPI) to help the Pixel-Level Attention Module (PLAM) preserve local image features in semi-supervised LViT setting. In our model, LV (Language-Vision) loss is designed to supervise the training of unlabeled images using text information directly. For evaluation, we construct three multimodal medical segmentation datasets (image + text) containing X-rays and CT images. Experimental results show that our proposed LViT has superior segmentation performance in both fully-supervised and semi-supervised setting. The code and datasets are available at https://github.com/HUANGLIZI/LViT.Comment: Accepted by IEEE Transactions on Medical Imaging (TMI

LViT: Language meets Vision Transformer in Medical Image Segmentation

Deep learning has been widely used in medical image segmentation and other aspects. However, the performance of existing medical image segmentation models has been limited by the challenge of obtaining sufficient high-quality labeled data due to the prohibitive data annotation cost. To alleviate this limitation, we propose a new text-augmented medical image segmentation model LViT (Language meets Vision Transformer). In our LViT model, medical text annotation is incorporated to compensate for the quality deficiency in image data. In addition, the text information can guide to generate pseudo labels of improved quality in the semi-supervised learning. We also propose an Exponential Pseudo label Iteration mechanism (EPI) to help the Pixel-Level Attention Module (PLAM) preserve local image features in semi-supervised LViT setting. In our model, LV (Language-Vision) loss is designed to supervise the training of unlabeled images using text information directly. For evaluation, we construct three multimodal medical segmentation datasets (image + text) containing X-rays and CT images. Experimental results show that our proposed LViT has superior segmentation performance in both fully-supervised and semi-supervised setting. The code and datasets are available at https://github.com/HUANGLIZI/LViT

Phyllanthus emblica aqueous extract retards hepatic steatosis and fibrosis in NAFLD mice in association with the reshaping of intestinal microecology

Accumulating evidence suggests that dysregulation of the intestinal flora potentially contributes to the occurrence and development of nonalcoholic fatty liver disease (NAFLD). Phyllanthus emblica (PE), an edible and medicinal natural resource, exerts excellent effects on ameliorating NAFLD, but the potential mechanism remains unclear. In the present study, a mouse NAFLD model was established by administering a choline-deficient, L-amino acid-defined, high-fat diet (CDAHFD). The protective effects of the aqueous extract of PE (AEPE) on the gut microbiota and fecal metabolites in NAFLD mice were detected by performing 16S rRNA gene sequencing and untargeted metabolomics. The administration of middle- and high-dose AEPE decreased the levels of ALT, AST, LDL-C, TG, and Hyp and increased HDL-C levels in CDAHFD-fed mice. Hematoxylin–eosin (H&E), Oil Red O, and Masson’s trichrome staining indicated that AEPE treatment attenuated hepatic steatosis and fibrotic lesions. Moreover, the disordered intestinal microflora was remodeled by AEPE, including decreases in the abundance of Peptostreptococcaceae, Faecalibaculum, and Romboutsia. The untargeted metabolomics analysis showed that AEPE restored the disturbed glutathione metabolism, tryptophan metabolism, taurine and hypotaurine metabolism, and primary bile acid biosynthesis of the gut bacterial community in NAFLD mice, which strongly correlated with hepatic steatosis and fibrosis. Collectively, AEPE potentially ameliorates NAFLD induced by a CDAHFD through a mechanism associated with its modulatory effects on the gut microbiota and microbial metabolism

SOT-MRAM-Enabled Probabilistic Binary Neural Networks for Noise-Tolerant and Fast Training

We report the use of spin-orbit torque (SOT) magnetoresistive random-access memory (MRAM) to implement a probabilistic binary neural network (PBNN) for resource-saving applications. The in-plane magnetized SOT (i-SOT) MRAM not only enables field-free magnetization switching with high endurance (> 10^11), but also hosts multiple stable probabilistic states with a low device-to-device variation (< 6.35%). Accordingly, the proposed PBNN outperforms other neural networks by achieving an 18* increase in training speed, while maintaining an accuracy above 97% under the write and read noise perturbations. Furthermore, by applying the binarization process with an additional SOT-MRAM dummy module, we demonstrate an on-chip MNIST inference performance close to the ideal baseline using our SOT-PBNN hardware

Interplay between moment-dependent and field-driven unidirectional magnetoresistance in CoFeB/InSb/CdTe heterostructures

Magnetoresistance effects are crucial for understanding the charge/spin transport as well as propelling the advancement of spintronic applications. Here we report the coexistence of magnetic moment-dependent (MD) and magnetic field-driven (FD) unidirectional magnetoresistance (UMR) effects in CoFeB/InSb/CdTe heterostructures. The strong spin-orbital coupling of InSb and the matched impedance at the CoFeB/InSb interface warrant a distinct MD-UMR effect at room temperature, while the interaction between the in-plane magnetic field and the Rashba effect at the InSb/CdTe interface induces the marked FD-UMR signal that dominates the high-field region. Moreover, owning to the different spin transport mechanisms, these two types of nonreciprocal charge transport show opposite polarities with respect to the magnetic field direction, which further enable an effective phase modulation of the angular-dependent magnetoresistance. Besides, the demonstrations of both the tunable UMR response and two-terminal spin-orbit torque-driven magnetization switching validate our CoFeB/InSb/CdTe system as a suitable integrated building block for multifunctional spintronic device design

Diffusion Tensor Imaging Detects Microstructural Differences of Visual Pathway in Patients With Primary Open-Angle Glaucoma and Ocular Hypertension

Ocular hypertension (OHT), the common situation in adult patients in the outpatients, occurs ∼5% worldwide. However, there are still some practical problems in differentiation of OHT with early primary open-angle glaucoma (POAG) using current standard methods. Application of high resolution diffusion tensor imaging (DTI) enables us to the differentiate axonal architecture of visual pathway between POAG and OHT subjects. Among 32 POAG patients recruited (15 OHT and 14 control subjects), 62.5% of glaucoma were in early stage for the current study. All subjects underwent ophthalmological assessments with standard automated perimetry and optical coherence tomography (OCT). DTI was applied to measure fraction anisotropy (FA) and mean diffusivity (MD) of optic tract (OT), lateral geniculate body (LGN) and optic radiation (OR) using voxel-based analysis. Our data demonstrated that FA values of bilateral OR in POAG were significantly lower in the right or left than that of OHT patients (left OR: 0.51 ± 0.04 vs. 0.54 ± 0.03, p &lt; 0.05; right OR: 0.51 ± 0.05 vs. 0.54 ± 0.03, p &lt; 0.05). In right LGN, MD values were higher in POAG patients compared with OHT subjects (9.81 ± 1.45 vs. 8.23 ± 0.62, p &lt; 0.05). However, no significant difference of all of the DTI parameters was observed between OHT and control subjects. DTI parameters in POAG patients were positively correlated with morphological and functional measurements (p &lt; 0.05). Vertical cup to disc ratio (VCDR) was correlated with ipsilateral FA of OT (p &lt; 0.05), ipsilateral MD of OT (p &lt; 0.05), ipsilateral MD of LGN (p &lt; 0.05), and contralateral MD of OT (p &lt; 0.05). Mean deviation of visual field (MDVF) was correlated with ipsilateral FA of OT (p &lt; 0.05), ipsilateral MD of OT (p &lt; 0.05), and ipsilateral FA of LGN (p &lt; 0.05). Our study demonstrated that DTI can differentiate POAG from OHT subjects in optic pathway, particularly in early POAG, and DTI parameters can quantify the progression of POAG

Multi-site, Multi-domain Airway Tree Modeling (ATM'22): A Public Benchmark for Pulmonary Airway Segmentation

Open international challenges are becoming the de facto standard for assessing computer vision and image analysis algorithms. In recent years, new methods have extended the reach of pulmonary airway segmentation that is closer to the limit of image resolution. Since EXACT'09 pulmonary airway segmentation, limited effort has been directed to quantitative comparison of newly emerged algorithms driven by the maturity of deep learning based approaches and clinical drive for resolving finer details of distal airways for early intervention of pulmonary diseases. Thus far, public annotated datasets are extremely limited, hindering the development of data-driven methods and detailed performance evaluation of new algorithms. To provide a benchmark for the medical imaging community, we organized the Multi-site, Multi-domain Airway Tree Modeling (ATM'22), which was held as an official challenge event during the MICCAI 2022 conference. ATM'22 provides large-scale CT scans with detailed pulmonary airway annotation, including 500 CT scans (300 for training, 50 for validation, and 150 for testing). The dataset was collected from different sites and it further included a portion of noisy COVID-19 CTs with ground-glass opacity and consolidation. Twenty-three teams participated in the entire phase of the challenge and the algorithms for the top ten teams are reviewed in this paper. Quantitative and qualitative results revealed that deep learning models embedded with the topological continuity enhancement achieved superior performance in general. ATM'22 challenge holds as an open-call design, the training data and the gold standard evaluation are available upon successful registration via its homepage.Comment: 32 pages, 16 figures. Homepage: https://atm22.grand-challenge.org/. Submitte