Search

Your search keyword '"Ren, Xing"' showing total 717 results
Start Over Author "Ren, Xing"
717 results on '"Ren, Xing"'

3. Stacking Group Structure of Fermionic Symmetry-Protected Topological Phases

Author

Ren, Xing-Yu, Ning, Shang-Qiang, Qi, Yang, Wang, Qing-Rui, and Gu, Zheng-Cheng

Subjects
Condensed Matter - Strongly Correlated Electrons
Abstract

In the past decade, there has been a systematic investigation of symmetry-protected topological (SPT) phases in interacting fermion systems. Specifically, by utilizing the concept of equivalence classes of finite-depth fermionic symmetric local unitary (FSLU) transformations and the fluctuating decorated symmetry domain wall picture, a large class of fixed-point wave functions have been constructed for fermionic SPT (FSPT) phases. Remarkably, this construction coincides with the Atiyah-Hirzebruch spectral sequence, enabling a complete classification of FSPT phases. However, unlike bosonic SPT phases, the stacking group structure in fermion systems proves to be much more intricate. The construction of fixed-point wave functions does not explicitly provide this information. In this paper, we employ FSLU transformations to investigate the stacking group structure of FSPT phases. Specifically, we demonstrate how to compute stacking FSPT data from the input FSPT data in each layer, considering both unitary and anti-unitary symmetry, up to 2+1 dimensions. As concrete examples, we explicitly compute the stacking group structure for crystalline FSPT phases in all 17 wallpaper groups and the mixture of wallpaper groups with onsite time-reversal symmetry using the fermionic crystalline equivalence principle. Importantly, our approach can be readily extended to higher dimensions, offering a versatile method for exploring the stacking group structure of FSPT phases., Comment: Correct some errors in 1+1D case. Add more examples for 2+1D crystalline FSPT protected by symmetries being the mixture of wallpaper groups with onsite time-reversal symmetry

Published
2023

4. Low-carbon optimal dispatch of integrated energy system considering demand response under the tiered carbon trading mechanism

Author

Wang, Limeng, Liu, Xuemeng, Li, Yang, Chang, Duo, and Ren, Xing

Subjects
Electrical Engineering and Systems Science - Systems and Control
Abstract

In the operation of the integrated energy system (IES), considering further reducing carbon emissions, improving its energy utilization rate, and optimizing and improving the overall operation of IES, an optimal dispatching strategy of integrated energy system considering demand response under the stepped carbon trading mechanism is proposed. Firstly, from the perspective of demand response (DR), considering the synergistic complementarity and flexible conversion ability of multiple energy sources, the lateral time-shifting and vertical complementary alternative strategies of electricity-gas-heat are introduced and the DR model is constructed. Secondly, from the perspective of life cycle assessment, the initial quota model of carbon emission allowances is elaborated and revised. Then introduce a tiered carbon trading mechanism, which has a certain degree of constraint on the carbon emissions of IES. Finally, the sum of energy purchase cost, carbon emission transaction cost, equipment maintenance cost and demand response cost is minimized, and a low-carbon optimal scheduling model is constructed under the consideration of safety constraints. This model transforms the original problem into a mixed integer linear problem using Matlab software, and optimizes the model using the CPLEX solver. The example results show that considering the carbon trading cost and demand response under the tiered carbon trading mechanism, the total operating cost of IES is reduced by 5.69% and the carbon emission is reduced by 17.06%, which significantly improves the reliability, economy and low carbon performance of IES., Comment: Accepted by Electric Power Construction [in Chinese]

Published
2023
Full Text
View/download PDF

32. ECG-based Atrial Fibrillation Detection Based on Deep Convolutional Residual Neural Network

Author

ZHAO Ren-xing, XU Pin-jie, LIU Yao

Subjects
atrial fibrillation, residual network, convolutional neural network, electrocardiogram, automatic detection, Computer software, QA76.75-76.765, Technology (General), T1-995
Abstract

In the context of increasing demand for intelligent diagnosis,a convolutional neural network model based on residual network is proposed for ECG(electrocardiogram) signal classification of atrial fibrillation.MIT-BIH atrial fibrillation data is used to verify the effectiveness of the method,and then assist the automatic detection of atrial fibrillation.Aiming at the problem of ECG signal dichotomy,firstly,the atrial fibrillation data set and previous data preprocessing work are introduced.Then,the processed data is input into the deep learning model constructed with convolutional neural network,to automatically extract features of atrial fibrillation from electrocardiogram signals.Finally,the designed deep learning model is used for atrial fibrillation detection.The validation of the method is proved with five cross-validation strategy.Performance of the classification is represented by the sensitivity,specificity,positive predictive value and accuracy,they are 99.26%,99.42%,99.61% and 99.47%,respectively.Then the performance of the proposed model and existing models are compared to confirm that the proposed model is feasible in atrial fibrillation detection.In conclusion,the automatic detection system for atrial fibrillation based on convolutional neural network with residual network can achieve a good classification performance of atrial fibrillation,which can be helpful in automatic atrial fibrillation detection.

Published
2022
Full Text
View/download PDF

39. Hypermethylated gene ANKDD1A is a candidate tumor suppressor that interacts with FIH1 and decreases HIF1α stability to inhibit cell autophagy in the glioblastoma multiforme hypoxia microenvironment

Author

Feng, Jianbo, Zhang, Yan, She, Xiaoling, Sun, Yingnan, Fan, Li, Ren, Xing, Fu, Haijuan, Liu, Changhong, Li, Peiyao, Zhao, Chunhua, Liu, Qiang, Liu, Qing, Li, Guiyuan, and Wu, Minghua

Subjects
Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Brain Disorders, Genetics, Rare Diseases, Neurosciences, Cancer, Orphan Drug, Brain Cancer, 2.1 Biological and endogenous factors, Aetiology, Ankyrin Repeat, Apoptosis, Autophagy, Cell Line, Tumor, Chromosomes, Human, Pair 15, DNA Methylation, Gene Expression Regulation, Neoplastic, Genes, Tumor Suppressor, Glioblastoma, Glucose, Half-Life, Humans, Hypoxia-Inducible Factor 1, alpha Subunit, Intracellular Signaling Peptides and Proteins, Lactates, Mixed Function Oxygenases, Neoplasm Proteins, Protein Stability, Recombinant Proteins, Repressor Proteins, Transcription, Genetic, Tumor Hypoxia, Tumor Microenvironment, Tumor Suppressor Proteins, Up-Regulation, Von Hippel-Lindau Tumor Suppressor Protein, Clinical Sciences, Oncology & Carcinogenesis, Biochemistry and cell biology, Oncology and carcinogenesis
Abstract

Ectopic epigenetic mechanisms play important roles in facilitating tumorigenesis. Here, we first demonstrated that ANKDD1A is a functional tumor suppressor gene, especially in the hypoxia microenvironment. ANKDD1A directly interacts with FIH1 and inhibits the transcriptional activity of HIF1α by upregulating FIH1. In addition, ANKDD1A decreases the half-life of HIF1α by upregulating FIH1, decreases glucose uptake and lactate production, inhibits glioblastoma multiforme (GBM) autophagy, and induces apoptosis in GBM cells under hypoxia. Moreover, ANKDD1A is highly frequently methylated in GBM. The tumor-specific methylation of ANKDD1A indicates that it could be used as a potential epigenetic biomarker as well as a possible therapeutic target.

Published
2019

46. Intradermal injection of Cutibacterium acnes and staphylococcus: A pustular acne‐like murine model.

Author

Zhou, Na, Sun, Yanan, Ren, Xing, Wang, Yunong, Gao, Xinyu, Li, Lishuang, Ma, Yuman, Hao, Yu, and Wang, Yi

Subjects
CUTIBACTERIUM acnes, INTRADERMAL injections, STAPHYLOCOCCUS, DRUG efficacy, MIXED infections, ACNEIFORM eruptions
Abstract

Background: Skin 16S microbiome diversity analysis indicates that the Staphylococcus genus, especially Staphylococcus aureus (S. aureus), plays a crucial role in the inflammatory lesions of acne. However, current animal models for acne do not fully replicate human diseases, especially pustular acne, which limits the development of anti‐acne medications. Aims: The aim is to develop a mouse model for acne, establishing an animal model that more closely mimics the clinical presentation of pustular acne. This will provide a new research platform for screening anti‐acne drugs and evaluating the efficacy of clinical anti‐acne experimental treatments. Methods: Building upon the existing combination of acne‐associated Cutibacterium acnes (C. acnes) with artificial sebum, we will inject a mixture of S. aureus and C. acnes locally into the dermis in a 3:7 ratio. Results: We found that the acne animal model with mixed bacterial infection better replicates the dynamic evolution process of human pustular acne. Compared to the infection with C. acnes alone, mixed bacterial infection resulted in pustules with a distinct yellowish appearance, resembling pustular acne morphology. The lesions exhibited redness, vascular dilation, and noticeable congestion, along with evident infiltration of inflammatory cells. This induced higher levels of inflammation, as indicated by a significant increase in the secretion of inflammatory factors such as IL‐1β and TNF‐α. Conclusion: This model can reflect the clinical symptoms and development of human pustular acne, overcoming the limitations of animal models commonly used in basic research to study this situation. It provides support for foundational research and the development of new acne medications. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results