Adaptive multi-grid FE simulation on dynamic damage and seismic failure of concrete structures

This paper presents a new adaptive multi-grid method for analyses on damage and failure in concrete column under cyclic loading. Self-adaptation of the method can carry out automatically coupling analysis on the process of evolving damage to structural failure with dynamic grid-change due to damage, without user intervention in the computation. The theory of multi-grid FEM coupled evolving damage is developed on the basis of the improved variational principle to consider damage evolution, in which the elements in each sub-domain with different grid sizes are under the different state of damage. Then the multi-grid FEM method is provided with the theory and a 3D adaptive mesh refinement procedure. As a case study of the method, the process of evolving damage to failure of a concrete column under cyclic loading is simulated by using the developed method, and the simulated results fit well with the experimental data. The results show that, the developed method is reliable in simulation on evolving damage and failure in concrete column under dynamic seismic loading with lower cost and sufficient precision

Dynamic stress intensity factor of a finite crack based on a fractional differential model

Fractional differential constitutive models are introduced for transient problem of Mode III finite length crack in a viscoelastic medium. The basic equations which govern the deformation behavior are converted to fractional wave-like equations. Integral transform method reduces the problem to Fredholm integral equation of second kind. Dynamics stress intensity factors of Mode III finite crack based on fractional differential constitutive are obtained by numerical solution of Fredholm integral equation

Adaptive dynamic multi-grid method for simulation on seismic damage evolution of concrete column

A new adaptive dynamic multi-grid method is developed for simulation on evolving damage in concrete column under seismic loading. The method should possess adaptive capability in order to carry out automatically coupling analysis without user intervention in the computation. As a case study of the method, the process of evolving damage to failure of a concrete column under seismic loading is simulated, and the simulated results fit well with the experiment. It shows that, the developed method can be used to reveal the seismic failure mechanism of concrete structures by considering the dynamic coupling process from material damage in concrete of stress concentration zone to local failure in vulnerable component and eventually to structural failure with the adaptive capability as well as better computational efficiency

Properties of Heavy Higgs Bosons and Dark Matter under Current Experimental Limits in the μ\muNMSSM

Searches for new particles beyond the Standard Model (SM) are an important task for the Large Hadron Collider (LHC). In this paper, we investigate the properties of the heavy non-SM Higgs bosons in the $\mu$-term extended Next-to-Minimal Supersymmetric Standard Model ($\mu$NMSSM). We scan the parameter space of the $\mu$NMSSM considering the basic constraints from Higgs data, dark matter (DM) relic density, and LHC searches for sparticles. And we also consider the constraints from the LZ2022 experiment and the muon anomaly constraint at 2$\sigma$ level. We find that the LZ2022 experiment has a strict constraint on the parameter space of the $\mu$NMSSM, and the limits from the DM-nucleon spin-independent (SI) and spin-dependent (SD) cross-sections are complementary. Then we discuss the exotic decay modes of heavy Higgs bosons decaying into SM-like Higgs boson. We find that for doublet-dominated Higgs $h_3$ and $A_2$, the main exotic decay channels are $h_3\rightarrow Z A_1$, $h_3\rightarrow h_1 h_2$, $A_2\rightarrow A_1 h_1$ and $A_2\rightarrow Z h_2$, and the branching ratio can reach to about 23$\%$, 10$\%$, 35$\%$ and 10$\%$ respectively. At the 13 TeV LHC, the production cross-section of $ggF\rightarrow h_3\rightarrow h_1 h_2$ and $ggF\rightarrow A_2\rightarrow A_1 h_1$ can reach to about $10^{-11}$pb and $10^{-10}$pb, respectively

Semi-Supervised Disease Classification based on Limited Medical Image Data

In recent years, significant progress has been made in the field of learning from positive and unlabeled examples (PU learning), particularly in the context of advancing image and text classification tasks. However, applying PU learning to semi-supervised disease classification remains a formidable challenge, primarily due to the limited availability of labeled medical images. In the realm of medical image-aided diagnosis algorithms, numerous theoretical and practical obstacles persist. The research on PU learning for medical image-assisted diagnosis holds substantial importance, as it aims to reduce the time spent by professional experts in classifying images. Unlike natural images, medical images are typically accompanied by a scarcity of annotated data, while an abundance of unlabeled cases exists. Addressing these challenges, this paper introduces a novel generative model inspired by H\"older divergence, specifically designed for semi-supervised disease classification using positive and unlabeled medical image data. In this paper, we present a comprehensive formulation of the problem and establish its theoretical feasibility through rigorous mathematical analysis. To evaluate the effectiveness of our proposed approach, we conduct extensive experiments on five benchmark datasets commonly used in PU medical learning: BreastMNIST, PneumoniaMNIST, BloodMNIST, OCTMNIST, and AMD. The experimental results clearly demonstrate the superiority of our method over existing approaches based on KL divergence. Notably, our approach achieves state-of-the-art performance on all five disease classification benchmarks. By addressing the limitations imposed by limited labeled data and harnessing the untapped potential of unlabeled medical images, our novel generative model presents a promising direction for enhancing semi-supervised disease classification in the field of medical image analysis

An Electronic Auction Scheme Based on Group Signatures and Partially Blind Signatures

AbstractA new electronic auction scheme is proposed based on group signatures and partially blind signatures. At the same security strengthen, an optimization was done on the processes of electronic auction scheme and the dependence on trusted third party was reduced, moreover, multiple goods is auctioned at the same time, therefore, this scheme suited to large-scale electronic auction. Furthermore, due to application of vickrey auctions, the principle of optimal allocation of goods is easily satisfied