On Constructing the Analytical Solutions for Localizations in a Slender Cylinder Composed of an Incompressible Hyperelastic Material

In this paper, we study the localization phenomena in a slender cylinder composed of an incompressible hyperelastic material subjected to axial tension. We aim to construct the analytical solutions based on a three-dimensional setting and use the analytical results to describe the key features observed in the experiments by others. Using a novel approach of coupled series-asymptotic expansions, we derive the normal form equation of the original governing nonlinear partial differential equations. By writing the normal form equation into a first-order dynamical system and with the help of the phase plane, we manage to solve two boundary-value problems analytically. The explicit solution expressions (in terms of integrals) are obtained. By analyzing the solutions, we find that the width of the localization zone depends on the material parameters but remains almost unchanged for the same material in the post-peak region. Also, it is found that when the radius-length ratio is relatively small there is a snap-back phenomenon. These results are well in agreement with the experimental observations. Through an energy analysis, we also deduce the preferred configuration and give a prediction when a snap-through can happen. Finally, based on the maximum-energy-distortion theory, an analytical criterion for the onset of material failure is provided.Comment: 27 pages 10 figure

A coordinated operation method of wind-PV-hydrogen-storage multi-agent energy system

Wind-photovoltaic (PV)-hydrogen-storage multi-agent energy systems are expected to play an important role in promoting renewable power utilization and decarbonization. In this study, a coordinated operation method was proposed for a wind-PV- hydrogen-storage multi-agent energy system. First, a coordinated operation model was formulated for each agent considering peer-to-peer power trading. Second, a coordinated operation interactive framework for a multi-agent energy system was proposed based on the theory of the alternating direction method of multipliers. Third, a distributed interactive algorithm was proposed to protect the privacy of each agent and solve coordinated operation strategies. Finally, the effectiveness of the proposed coordinated operation method was tested on multi-agent energy systems with different structures, and the operational revenues of the wind power, PV, hydrogen, and energy storage agents of the proposed coordinated operation model were improved by approximately 59.19%, 233.28%, 16.75%, and 145.56%, respectively, compared with the independent operation model

Topological Atomic Spinwave Lattices by Dissipative Couplings

Recent experimental advance in creating dissipative couplings provides a new route for engineering exotic lattice systems and exploring topological dissipation. Using the spatial lattice of atomic spinwaves in a vacuum vapor cell, where purely dissipative couplings arise from diffusion of atoms, we experimentally realize a dissipative version of the Su-Schrieffer-Heeger (SSH) model. We construct the dissipation spectra of the topological or trivial lattices via electromagnetically-induced-transparency (EIT) spectroscopy. The topological dissipation spectrum is found to exhibit edge modes at dissipation rates within a dissipative gap, decoupled from the bulk. We also validate chiral symmetry of the dissipative SSH couplings. This work paves the way for realizing topology-enabled quantum correlations and non-Hermitian topological quantum optics via dissipative couplings.Comment: 5 pages, 4 figure

Understanding the Mechanism of Deep Learning Frameworks in Lesion Detection for Pathological Images with Breast Cancer

With the advances of scanning sensors and deep learning algorithms, computational pathology has drawn much attention in recent years and started to play an important role in the clinical workflow. Computer-aided detection (CADe) systems have been developed to assist pathologists in slide assessment, increasing diagnosis efficiency and reducing misdetections. In this study, we conducted four experiments to demonstrate that the features learned by deep learning models are interpretable from a pathological perspective. In addition, classifiers such as the support vector machine (SVM) and random forests (RF) were used in experiments to replace the fully connected layers and decompose the end-to-end framework, verifying the validity of feature extraction in the convolutional layers. The experimental results reveal that the features learned from the convolutional layers work as morphological descriptors for specific cells or tissues, in agreement with the diagnostic rules in practice. Most of the properties learned by the deep learning models summarized detection rules that agree with those of experienced pathologists. The interpretability of deep features from a clinical viewpoint not only enhances the reliability of AI systems, enabling them to gain acceptance from medical experts, but also facilitates the development of deep learning frameworks for different tasks in pathological analytics

Influence of synthetic superparamagnetic iron oxide on dendritic cells

Yongbin Mou1, Baoan Chen2, Yu Zhang3, Yayi Hou4, Hao Xie4, Guohua Xia2, Meng Tang5, Xiaofeng Huang1, Yanhong Ni1, Qingang Hu1,6 1Central Laboratory of Stomatology, Stomatological Hospital Affiliated Medical School, Nanjing University, 2Department of Hematology, Zhongda Hospital, Medical School, Southeast University, 3State Key Laboratory of Bioelectronics, Jiangsu Key Laboratory for Biomaterials and Devices, Southeast University, 4Immunology and Reproductive Biology Laboratory, Medical School, Nanjing University, 5Laboratory of Environmental Medicine and Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, People's Republic of China; 6Leeds Dental Institute, Faculty of Medicine and Health, University of Leeds, Leeds, UK Background: This study investigated the influence of synthetic superparamagnetic iron oxide (SPIO) on dendritic cells and provides a possible method for labeling these cells. Methods: SPIO nanoparticles were prepared, and their morphology and magnetic properties were characterized. The particles were endocytosed by dendritic cells generated from mouse bone marrow. Labeling efficiency and cellular uptake were analyzed by Prussian blue staining and quantitative spectrophotometric assay. Meanwhile, the surface molecules, cellular apoptosis, and functional properties of the SPIO-labeled dendritic cells were explored by flow cytometry and the mixed lymphocyte reaction assay. Results: The synthetic nanoparticles possessed a spherical shape and good superparamagnetic behavior. The mean concentration of iron in immature and mature dendritic cells was 31.8 ± 0.7 µg and 35.6 ± 1.0 µg per 1 × 106 cells, respectively. After 12 hours of incubation with SPIO at a concentration of 25 µg/mL, nearly all cells were shown to contain iron. Interestingly, cellular apoptosis and surface expression of CD80, CD86, major histocompatibility II, and chemokine receptor 7 in mature dendritic cells were not affected to any significant extent by SPIO labeling. T cell activation was maintained at a low ratio of dendritic cells to T cells. Conclusion: SPIO nanoparticles have good superparamagnetic behavior, highly biocompatible characteristics, and are suitable for use in further study of the migratory behavior and biodistribution of dendritic cells in vivo. Keywords: superparamagnetic iron oxide, dendritic cell, cell labelin

Unified Physical-Digital Face Attack Detection

Face Recognition (FR) systems can suffer from physical (i.e., print photo) and digital (i.e., DeepFake) attacks. However, previous related work rarely considers both situations at the same time. This implies the deployment of multiple models and thus more computational burden. The main reasons for this lack of an integrated model are caused by two factors: (1) The lack of a dataset including both physical and digital attacks with ID consistency which means the same ID covers the real face and all attack types; (2) Given the large intra-class variance between these two attacks, it is difficult to learn a compact feature space to detect both attacks simultaneously. To address these issues, we collect a Unified physical-digital Attack dataset, called UniAttackData. The dataset consists of $1,800$ participations of 2 and 12 physical and digital attacks, respectively, resulting in a total of 29,706 videos. Then, we propose a Unified Attack Detection framework based on Vision-Language Models (VLMs), namely UniAttackDetection, which includes three main modules: the Teacher-Student Prompts (TSP) module, focused on acquiring unified and specific knowledge respectively; the Unified Knowledge Mining (UKM) module, designed to capture a comprehensive feature space; and the Sample-Level Prompt Interaction (SLPI) module, aimed at grasping sample-level semantics. These three modules seamlessly form a robust unified attack detection framework. Extensive experiments on UniAttackData and three other datasets demonstrate the superiority of our approach for unified face attack detection.Comment: 12 pages, 8 figure

Clinical study of exhaled nitric oxide in children with asthma and allergic rhinitis

Objective·To determine the levels of nasally exhaled nitric oxide (FnNO) combined with fractional concentration of exhaled nitric oxide (FeNO) in children with asthma (AS) complicated with allergic rhinitis (AR), and analyze the levels of FnNO and FeNO in different clinical stages of AS with different severities of AR, so as to provide basis for guiding clinical diagnosis and treatment.Methods·Children diagnosed with AR with AS in the Department of Respiratory and Otolaryngology of Children's Hospital of Soochow University from April 2021 to November 2021 were included, and healthy children who visited the Department of Pediatrics for normal physical examination during the same period were enrolled as the control group. FeNO and FnNO were measured in all children to assess the severity of the children's diseases. The levels of FeNO and FnNO in children with AR and AS at different clinical stages of AS and their correlation with pulmonary function were compared and analyzed.Results·The proportion of persistent moderate-to-severe rhinitis was higher in the acute exacerbation stage of AS, and the proportion of intermittent mild rhinitis was higher in the clinical remission stage of AS. The FeNO level in the acute exacerbation stage were higher than that in the chronic persistent stage and clinical remission stage of AS (adjusted P=0.022, 0.000), and higher in the chronic persistent stage than that in the clinical remission stage of AS (adjusted P=0.002). The FnNO level in the acute exacerbation stage was higher than that in the clinical remission stage of AS (adjusted P=0.044). In the chronic persistent stage of AS, the FnNO levels in the persistent mild group and persistent moderate-to-severe control group were higher than those in the intermittent mild group (adjusted P=0.001, 0.000). In the clinical remission stage of AS, the FnNO levels in the persistent mild group and persistent moderate to severe control group were higher than those in the intermittent mild group (adjusted P=0.001, 0.007). In the intermittent mild group of AR, the FnNO levels in the acute exacerbation stage were higher than those in the chronic persistent stage and clinical remission stage of AS (adjusted P=0.010, 0.019). Part of pulmonary functions in the acute exacerbation stage of AS children were negatively correlated with the FeNO and FnNO levels (all P<0.05), while FEV1/pred in the chronic persistent stage was negatively correlated with FeNO level (P=0.010).Conclusion·FeNO and FnNO levels increased in the acute exacerbation stage of AS, and symptom scores of AR also increased. FeNO and FnNO levels were negatively correlated with pulmonary function in AS with AR children

Data on prevalence of atrial fibrillation and its association with stroke in low-, middle-, and high-income regions of China

Data presented in this article are supplementary material to our research article entitled " Prevalence of Atrial Fibrillation in Different Socioeconomic Regions of China and Its Association with Stroke: Results from a National Stroke Screening Survey" (Wang et al., 2018) [1]. This data article summarizes previous studies of Atrial Fibrillation (AF) prevalence in China, and estimates the association between AF and stroke in different socioeconomic regions of China through a national survey