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Location-based social networks (LBSNs) have greatly promoted the development of the field of human mobility mining. However, the sparsity, multimodality and heterogeneity nature of the user check-in data remains a great concern for learning high-quality user or other entities representations, especially in the downstream application tasks, such as point-of-interest (POI) recommendation. Most existing methods focus on user preference modeling based on sequential POI tags without exploring the interaction between different modalities (e.g., user-user interactions, user-timestamp interactions, user-POI interactions, etc.). To this end, we introduce a multimodal interaction aware embedding framework to generate reliable entity embeddings on the heterogeneous socio-spatial network. At its core, first, multi-modal interaction sub-graph sampling techniques are designed to capture the heterogeneous contexts; then, a self-supervised contrastive learning technique is leveraged to extract intra-modality and inter-modality interactions in a light way. We conduct experiments on the next-POI recommendation tasks based on three real-world datasets. Experimental results demonstrate the superiority of our model over the state-of-the-art embedding learning algorithms.

The swim bladder-derived material did not elicit significant aberrant immune responses by in vitro and in vivo assays, giving strong confidence for its application in tissue engineering or medical devices.

We have developed a CeO2−x-coated quaternary Ni-rich and Co-poor cathode to eliminate lattice oxygen emission and stabilize interface chemistry, enabling the long cycling and fast Li-ion diffusion of LIBs.

LANGUAGE NOTE | Document text in Chinese; abstract also in English. 唐文明教授的〈承認理論的創造性回歸一一一項關於人倫構成的哲學研究〉（以下簡稱〈承認理論〉）既體現了作者的理論雄心，也體現了作者的理論視野。自從霍耐特從黑格爾研究出發獨創性地發展出自己的承認理論以來，承認理論得到了廣泛的關注與討論，但是，既有研究的重心主要有兩點，一是從現代政治實踐出發，探討這種承認理論對於構建現代政治秩序具有的價值，二是從承認理論的問題史出發，揭示霍耐特承認理論與黑格爾的淵源及其異同。但〈承認理論〉的旨趣在於為人倫一政治結構中的承認奠基，以主奴辯證法為前提的現代承認理論指向的是一種作為欲望的主體之間的相互承認，而原初承認必須追溯到天人關係。(撮要取自內文首段) The purpose of the theory of recognition is to lay a foundation for recognition in the human relations political structure. Modern recognition theory is based in the dialectics of master and slave and examines mutual recognition between subjects as desires, although the original recognition must be traced back to the relationship between Heaven and man. Modern recognition theory emphasizes the relationship between “man's love for the divine foundation of existence” and political order. This article examines the theory of recognition, which may provide the foundation for a real community, from a perspective related to this issue. The article's weakness is that, when discussing Hegel's theory of recognition, it simply equates Hegel's self-consciousness with desire without analyzing its context and its connection with spirit.

With the rapid development of interactive 3D graphics technology, as well as the growing demand for virtual reality, digital urbanization and digital cultural heritage protection and time-consuming and inefficient traditional artificial building modeling methods have been far from meeting the rapid and intelligent needs of the application market and automatic. Architectural modeling methods have been paid more and more attention. Architectural modeling is an application-oriented comprehensive research field. According to different application scenarios, its research methods cover many technical fields and disciplines. This paper introduces a method of modeling ancient buildings using depth image estimation, spherical projection mapping, 3D adversarial generation network, and other techniques. The characteristics of architectural modeling methods are discussed from different disciplinary and technical perspectives. Second, the three major schools of architectural modeling technology, mainly the process modeling method, image modeling method, and point cloud modeling method, as well as the inverse process modeling method, which has attracted much attention and challenges in recent years, are summarized in detail. Then, the problem of building modeling is discussed. The problems and challenges of building modeling technology are analyzed, and the future development trend is predicted.

Obesity has become a global problem that poses a serious threat to human health. Laparoscopic sleeve gastrectomy (LSG) is an effective long-term treatment. However, the weight loss of some patients after LSG is still insufficient. It is necessary to investigate the factors associated with inadequate weight loss after LSG.The objective of this study was to explore whether preoperative insulin secretion could be associated with weight loss after LSG in patients with obesity.This is a single-center prospective cohort study conducted in a university hospital.Patients from a prospective database who underwent LSG were analyzed. All 178 participants underwent a 75-g oral glucose tolerance test (OGTT) to assess preoperative insulin and c-peptide secretion before LSG. The areas under the curve (AUCs) for glucose, insulin, and c-peptide were determined in the OGTT. The percentage of excess weight loss (%EWL) and the percentage of total weight loss (%TWL) were used to estimate the effect of weight loss after LSG. Regression models were used to assess the correlation between preoperative insulin and c-peptide secretion with %EWL ≥75% and TWL ≥35% at 12 months after LSG.The AUCs of insulin and c-peptide were significantly lower in the %EWL ≥75% and %TWL ≥35% groups at 0-30 minutes, 0-60 minutes, and 0-120 minutes during the OGTT. At 30, 60, and 120 minutes during the OGTT, c-peptide levels were significantly lower in the %EWL ≥75% group and %TWL ≥35% group. The preoperative c-peptide level at 30 minutes during the OGTT (Csub30/sub) was significantly negatively correlated with %EWL (β = -.37, Plt; .001) and %TWL (β = -.28, P = .011). Univariate logistic regression analysis showed that preoperative Csub30/subwas associated with %EWL ≥75% and %TWL ≥35% after LSG. According to multiple logistic regression analysis, patients with a low preoperative Csub30/subhad an 8-fold higher %TWL ≥35% after LSG than those with a high Csub30/sub(odds ratio: 8.41 [95% confidence interval: 1.46-48.58], P = .017). Similarly, patients with a low preoperative Csub30/subhad a 7-fold higher EWL% ≥75% after LSG than patients with a high Csub30/sub(odds ratio: 7.25 [95% confidence interval: 1.11-47.50], P = .039).The rate of weight loss after LSG is low among patients with preoperative hyperinsulinemia. The preoperative c-peptide level at 30 minutes during the OGTT is associated with weight loss after LSG.

Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease. Neutrophil extracellular traps (NETs) are a network structure composed of loose chromatin and embedded with multiple proteins. Here, we observed increased NETs deposition in the glomeruli of DKD patients and diabetic mice (streptozotocin-induced or db/db mice). After NETs were degraded with DNase I, diabetic mice exhibited attenuated glomerulopathy and glomerular endothelial cells (GECs) injury. We also observed alleviated glomerulopathy and GECs injury in peptidylarginine deiminase 4–knockout mice with streptozotocin-induced diabetes. In vitro, NETs-induced GECs pyroptosis was characterized by pore formation in the cell membrane, dysregulation of multiple genes involved in cell membrane function, and increased expression of pyroptosis-related proteins. Strengthening the GECs surface charge by oleylamine significantly inhibited NETs-induced GECs pyroptosis. These findings suggest that the GECs charge-related pyroptosis is involved in DKD progression, which is promoted by NETs.

To compare the ability of the modified Systemic Manifestation Score (mSMS) and the mPouchot score to distinguish adult-onset Still's disease (AOSD) with high disease severity in a large cohort.We scored the disease severity of 174 patients and categorized them into high and low disease severity states. The correlation of mSMS and mPouchot score with ESR, CRP, ferritin, liver function tests, and serum cytokines was investigated. Receiver operator characteristic (ROC) curve and logistic regression analysis were performed to compare the ability of mSMS and mPouchot to distinguish patients with severe AOSD.Both mSMS and mPouchot score were positively correlated with ESR (both P 0.001), CRP (both P 0.0001), and serum ferritin (both P 0.0001). Moreover, both mSMS and mPouchot score are significantly associated with liver dysfunction and high IL-18 (both P 0.0001) and IL-6 (both P 0.01) levels in AOSD patients. Furthermore, the area under curve (AUC) value of mSMS was significantly less than of mPouchot score (0.71 for mSMS, 0.81 for mPouchot score, P 0.0001). Compared with mPouchot score, mSMS had higher sensitivity (75.64% vs 74.36%) and lower specificity (55.06% vs 76.40%). And mSMS had a worse performance in assessing high disease severity than mPouchot score in logistic analysis.Both scores are proven as effective to assess disease severity of AOSD. By contrast, mSMS perform worse in assessing high disease severity of AOSD patients than mPouchot score. Key Points • Both modified Systemic Manifestation Score (mSMS) and modified Pouchot score (mPouchot score) positively correlated with ESR, CRP, and serum ferritin of AOSD patients. • Both scores are significantly associated with impaired liver function and high serum cytokine levels. • mSMS had lower discriminative ability than mPouchot score to distinguish high disease severity of AOSD patients.

Tax reduction and fee reduction are not only conducive to deepening the supply side structural reform, but also conducive to promoting the continuous improvement of the quality of China's economic growth. Based on the goal of high-quality economic development, this paper comprehensively combs the tax preferential policies issued by the state for small and micro enterprises, deeply analyzes the incentive mechanism of tax reduction and fee reduction for small and micro enterprises from the theoretical level, and empirically tests the incentive effect of tax reduction and fee reduction on small and micro enterprises through actual data. Finally, based on the theoretical mechanism analysis and empirical test results, this paper puts forward reasonable optimization suggestions for the tax reduction and fee reduction policies of small and micro enterprises under the impact of the epidemic, so as to promote the tax reduction and fee reduction work to take root and promote the stable and healthy development of small and micro enterprises.

A fuzzy set-valued information system (FSVIS) is a special information system (IS) where the value of an object under each attribute or each attribute value is a fuzzy set. Homomorphism is a powerful mathematical tool to deal with FSVISs, which can be used to study relationships among them. Based on data compression, we obtain some characterizations about FSVISs and their homomorphisms. First, some homomorphisms between FSVISs are introduced. After that, attribute reduction based on tolerance relation in a FSVIS is studied. Eventually, we get invariant characterizations of FSVISs based on some special homomorphisms under data compression.

For schools, the quality of teaching and learning is an important guarantee for achieving educational goals. There are very many factors affecting teaching quality, including hard and soft factors. Among them, the soft factors mainly refer to the teaching quality of teachers and the teaching management mechanism of schools. The teaching quality of teachers is the most critical among all factors. Traditional teaching quality evaluation (TQE) mainly adopts the way of manual scoring. This way of TQE lacks data support, and there is no evaluation mechanism based on multiple data sources. Therefore, the TQE results obtained in this way are subjective and inaccurate. Considering that the main subjects of the whole teaching session are teachers, students, and schools, each subject generates massive information in the whole teaching process. In our study, a novel evaluation method based on fuzzy classification algorithm is proposed to be applied in teaching evaluation. Firstly, this paper collects the relevant data generated by the three subjects in the teaching process, respectively. Secondly, after removing the unsuitable data, principal component analysis is used to extract the main features of the applicable data. Finally, a fuzzy support vector machine (FSVM) is used to classify and analyze the feature data in order to derive each teacher’s teaching evaluation results. The final evaluation results in this paper are divided into five grades, i.e., five categories: excellent, good, moderate, passing, and failing. The comparison with other algorithms demonstrates that the teaching evaluation model based on fuzzy comprehensive evaluation algorithm used in this paper has the advantages of high evaluation accuracy and objectivity, and we hope that this study will be useful in the field of teaching evaluation.

Background The stalk traits (stalk diameter (SD), stalk length (SL), rind penetrometer resistance (RPR), and dry matter content (DMC)) are important indicators for measuring lodging resistance. Results In this study, 377 inbred lines were used as four stalk-related traits, including SD, SL, RPR and DMC, were investigated at the tasseling, grain filling, and maturity stages in the third segment of maize. 461053 high-quality SNPs which were obtained by whole genome resequencing was used for genome-wide association study. The results showed that 29 significant SNPs related to traits were detected by MLM analysis (P − 6), related to stalk correlation explaining 7.19% -15.03% of phenotypic variation, among which 4, 1, 4 and 20 SNPs were found related to RPR, SD, SL, and DMC respectively. Most candidate genes are related to plant element structure, signal transduction mechanisms, inorganic ion transport and metabolism, nucleotide transport and metabolism, and transporter enzyme families. Comparing MLM and GLM statistical models, a total of 12 candidate genes were detected repeatedly, with candidate gene Zm00001d014449 being detected 5 times, with a phenotypic variation interpretation rate of 9.95% -10.84%. This gene is mainly expressed in cells with active cell division and tissue differentiation, and is involved in the formation of stalk vascular bundles and the synthesis of cell walls. Another candidate gene Zm00001d005300 encodes the transcription factor MYB44, which regulates the dependence of salt stress signal phosphorylation, can effectively inhibit the accumulation of destructive reactive oxygen species, and has a certain resistance to non-Biotic stress. In addition, this study also found that 10 unknown functional genes can be further Functional verification. Conclusions This study helps to deepen the understanding of the genetic basis of maize stalk lodging resistance related traits, and provides theoretical guidance for future maize lodging resistance breeding.

Multi-robot system for manufacturing is an Industry Internet of Things (IIoT) paradigm with significant operational cost savings and productivity improvement, where Unmanned Aerial Vehicles (UAVs) are employed to control and implement collaborative productions without human intervention. This mission-critical system relies on 3-Dimension (3-D) scene recognition to improve operation accuracy in the production line and autonomous piloting. However, implementing 3-D point cloud learning, such as Pointnet, is challenging due to limited sensing and computing resources equipped with UAVs. Therefore, we propose a Digital Twin (DT) empowered Knowledge Distillation (KD) method to generate several lightweight learning models and select the optimal model to deploy on UAVs. With a digital replica of the UAVs preserved at the edge server, the DT system controls the model sharing network topology and learning model structure to improve recognition accuracy further. Moreover, we employ network calculus to formulate and solve the model sharing configuration problem toward minimal resource consumption, as well as convergence. Simulation experiments are conducted over a popular point cloud dataset to evaluate the proposed scheme. Experiment results show that the proposed model sharing scheme outperforms the individual model in terms of computing resource consumption and recognition accuracy., 16 pages, 12 figures, journal

As a major component of the extracellular matrix, collagen has been used as a biomaterial for many purposes including tissue engineering. Commercial collagen derived from mammals is associated with a risk of prion diseases and religious restrictions, while fish-derived collagen can avoid such issues. In addition, fish-derived collagen is widely available and low-cost; however, it often suffers from poor thermal stability, which limits its biomedical application. In this study, collagen with a high thermal stability was successfully extracted from the swim bladder of silver carp (Hypophthalmichthys molitrix) (SCC). The results demonstrated that it was a type I collagen with high purity and well-preserved triple-helix structure. Amino acid composition assay showed that the amounts of threonine, methionine, isoleucine and phenylalanine in the collagen of swim bladder of silver carp were higher than those of bovine pericardium. After adding salt solution, swim-bladder-derived collagen could form fine and dense collagen fibers. In particular, SCC exhibited a higher thermal denaturation temperature (40.08 °C) compared with collagens from the swim bladder of grass carp (Ctenopharyngodon idellus) (GCC, 34.40 °C), bovine pericardium (BPC, 34.47 °C) and mouse tail (MTC, 37.11 °C). Furthermore, SCC also showed DPPH radical scavenging ability and reducing power. These results indicate that SCC presents a promising alternative source of mammalian collagen for pharmaceutical and biomedical applications.

BRAF is a key member in the MAPK signaling pathway essential for cell growth, proliferation, and differentiation. Dysregulation or mutation of BRAF is often the underlying cause of various types of cancer. RAS, a small GTPase protein that acts upstream of BRAF, has been identified as a driver of up to one-third of all cancers. When BRAF interacts with RAS via the RAS binding domain (RBD) and membrane recruitment, BRAF undergoes a conformational change from an inactive, autoinhibited monomer to an active dimer and subsequently phosphorylates MEK to propagate the signal. BRAF domains are involved in specific functions of the regulatory mechanism, as exampled by maintenance of the autoinhibited conformation through interactions between the Cysteine Rich Domain (CRD) and the Kinase Domain (KD) of BRAF. Despite the central role of BRAF in cellular signaling, the exact order and magnitude of its activation steps has yet to be confirmed experimentally. We employed pulldown assays, open surface plasmon resonance (OpenSPR), and hydrogen-deuterium exchange mass spectrometry (HDX-MS) to investigate the roles of the regulatory regions in BRAF activation and autoinhibition. Our results demonstrate that the BRAF specific region (BSR) and CRD play a crucial role in regulating the activity of BRAF. Moreover, we quantified the autoinhibitory binding affinities between the N-terminal domains of BRAF and the KD and revealed the individual roles of the BRAF regulatory domains. Furthermore, we quantified the relief of autoinhibition between the N-terminal domains of BRAF and the KD upon RAS binding, providing direct evidence that RAS binding initiates RAF activation. Additionally, our findings provide evidence that the BSR negatively regulates BRAF activation in a RAS isoform-specific manner and highlight the importance of considering the specific isoform pairs when developing inhibitors targeting RAF-RAS interactions. Our findings also indicate that oncogenic BRAF-KDD594Gmutant has a lower affinity for the regulatory domains, implicating that pathogenic BRAF acts through decreased propensity for autoinhibition. Collectively, our study provides valuable insights into the activation mechanism of BRAF kinase and may help to guide the development of new therapeutic strategies for cancer treatment.

In the reign of Internet technology, cloud technology has emerged as one of the powerful tools in computing with many advantages in the bag. Cloud technology is defined as online access to all types of computing applications. The applications range from access to data, servers, software, databases to storage and retrieval of data. The data can be accessed from anywhere, anytime. The user does not need to own the hardware in this case. Clouds have multiple data centers located at various places. On-demand data service is the best part of cloud technology. This technology is made use in many industries. In this research, we will study how cloud technology and wireless networking technology are applied to optimize hospital financial management. The healthcare industry faces various challenges in financial management such as budgeting, growth planning, and cost-effectiveness. Considering this scenario, it is evident that there is a solid need to optimize its financial management. The cloud computing technology driven by artificial intelligence (AI) is deployed in carrying out repetitive tasks and automation, resulting in increased productivity. In this research, the proposed system uses wireless networking systems and cloud technology to optimize hospital financial management. The model works with the implementation of a hybrid of fuzzy neural networks. The proposed model is analyzed with the existing fuzzy logic and neural network models, and it is found that the proposed model has obtained an accuracy of 97.56% with minimum resources for successful financial management at hospitals.

As an important development strategy in China, digital economy has new kinetic energy to accelerate economic operation. Using digital economy to promote the development of ESG of Chinese enterprises has great potential. Facing many problems in the current development of enterprise ESG, such as high disclosure cost, lack of independent disclosure awareness and ability, imperfect mechanism and relevant policies, imperfect localization evaluation system and so on. We can improve the overall ESG disclosure level of enterprises by using digital technology to strengthen publicity, establish a systematic disclosure system and improve the disclosure system.

Tissue fibrosis is a key factor leading to disability and death worldwide; however, thus far, there are no approved treatments for fibrosis. Transforming growth factor (TGF)-β is a major pro-fibrotic cytokine, which is expected to become a target in the treatment of fibrosis; however, since TGF-β has a wide range of biological functions involving a variety of biological processes in the body, a slight change in TGF-β may have a systematic effect. Indiscriminate inhibition of TGF-β can lead to adverse reactions, which can affect the efficacy of treatment. Therefore, it has become very important to explore how both the TGF-β signaling pathway is inhibited and the safe and efficient TGF-β small molecule inhibitors or neutralizing antibodies are designed in the treatment of fibrotic diseases. In this review, we mainly discuss the key role of the TGF-β signaling pathway in fibrotic diseases, as well as the development of fibrotic drugs in recent years, and explore potential targets in the treatment of fibrotic diseases in order to guide subsequent drug development.

Zhihong Wang,1,&ast; Peng Tang,2,&ast; Surong Hua,1 Junyi Gao,1 Bin Zhang,3 Hua Wan,4 Qixi Wu,4 Jiaxin Zhang,5 Ge Chen1 1Department of General Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, People’s Republic of China; 2Department of Breast and Thyroid Surgery, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, People’s Republic of China; 3Department of General Surgery, Qilu Hospital of Shandong University, Jinan, People’s Republic of China; 4Research and Development Department, Beijing USCI Medical Laboratory, Beijing, People’s Republic of China; 5Department of Thyroid and Breast Surgery, Affiliated Hospital of Xuzhou Medical University, Xuzhou, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Ge ChenDepartment of General Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, No. 1 Shuai Fu Yuan Hu Tong, Beijing, 100730, People’s Republic of China, Tel +86 156 1123 3738, Fax +86 156 69152600, Email chenge@pumch.cn; Jiaxin ZhangDepartment of Thyroid and Breast Surgery, Affiliated Hospital of Xuzhou Medical University, No. 99, Huaihai West Road, Xuzhou, 221004, People’s Republic of China, Tel +86 180 5226 8693, Fax +86 180 85802306, Email zhangjiaxin1969@163.comPurpose: To describe the genetic landscape and clinical characteristics of Chinese patients diagnosed with papillary thyroid cancer (PTC) and to determine which high-risk genetic characteristics suggest a likelihood of lymph node metastasis (LNM) and lateral lymph node metastasis (LLNM).Patients and Methods: Data from previously untreated patients with PTC collected between May 2018 and December 2020 from 14 hospitals in China were analyzed retrospectively. High-risk pathologic characteristics were defined as T3/T4, N(+), and N1b(+) stages. All patients were tested for 57 genes by second-generation sequencing. The t-test, chi-square test, and Fisher’s exact test were performed for statistical analysis.Results: Overall, 395 patients were enrolled in this study. The prevalence of BRAF mutation was 78.53%. BRAF mutant allele frequency (MAF) > 16.93% was associated with a significantly higher risk of LNM, LLNM, and T3 + T4 stage compared with a low-risk group, defined by a MAF < 2.54% (odd ratios [ORs] for each risk=3.38, 3.46, and 8.54, respectively), and an intermediate-risk group, defined by a MAF of 2.54% to 16.93% (ORs=2.04, 2.07, and 4.07, respectively). The population with RET fusion had higher T, N, and N1b stages (ORs for each stage=10.40, 7.60, and 8.77, respectively) compared with a RET-negative population. Similar conclusions about T, N, and N1b stages were observed in relation to multiple driver gene mutations (ORs for each stage=7.48, 2.80, and 7.04, respectively) compared with population without multiple driver mutations. These genetic characteristics may be suggestive of high clinical risk. However, regardless of genetic profiles, patients younger than age 45 years had greater rates of LNM and LLNM.Conclusion: The main driver gene in this study, BRAF, differs significantly between the United States (79% vs 51%) and other countries. The Chinese population in this study that experienced more aggressive tumor biology had a BRAF MAF greater than 16.93%, exhibited RET fusion events, and had multiple driver gene mutations; thus, these traits may be considered high-risk genetic characteristics in PTC that could warrant aggressive treatment in such population.Keywords: papillary thyroid cancer, lymph node metastasis, high risk clinicopathological characteristics, genotypes

Using a sample of 14,245 firm-year observations from 2,579 listed firms in the Chinese capital market, this study investigates whether board directors with an educational background in science, technology, engineering, and mathematics (STEM) are associated with greater corporate innovation. The results suggest that such directors have significant beneficial effects on corporate innovation activities. This study provides novel empirical evidence supporting resource dependence theory and extends the labor economics literature regarding the value of STEM graduates for corporate innovation. The findings have significant implications for policymakers and practitioners, showing that firms with a strategic focus on innovation may find it beneficial to appoint more directors with a background in STEM.

The nozzle of solid rocket motor (SRM) is easily ablated by high temperature, high pressure, high speed, and corrosive particles, which affects the stability of rocket flight. Therefore, the measurement and characterization of the nozzle ablation rate are helpful in providing some guidance for the design of nozzle material and structure. However, due to the high surface roughness of the composite nozzle after ablation, it is difficult to obtain an accurate ablation rate by contact measurement methods. The 3D laser scanning system is a 3D non-contact measurement technology using structured light technology, phase measurement technology, and computer vision technology. It has the advantages of non-contact, large scanning size, flexibility, and portability. In this paper, a 3D reconstruction of the ablation nozzle is carried out based on the 3D laser scanning system. Additionally, the ablation rate of the nozzle is measured without cutting the actual specimen. Furthermore, the pressure, temperature, and surface convective heat transfer coefficient trends are numerically calculated and compared with the ablation rate. Additionally, the empirical formula between ablation rate and pressure, temperature, convective heat transfer coefficient is obtained empirically by the inversion analysis method. The empirical formula can provide theoretical guidance for nozzle size design and optimization. The results show that the non-contact 3D laser scanning system is a valuable method for reconstructing the model of the ablated nozzle, and the empirical formula of ablation rate can accurately predict the ablation rate of the nozzle.

As a key component of the solid rocket motor, nozzle ablation is very important to the performance of the motor. In order to study the influence of the law of nozzle ablation on motor performance, a two-dimensional axisymmetric model of a ground state nozzle and vacuum state nozzle before and after ablation was constructed on the basis of a reference profile, and the change law of pressure, temperature and velocity with ablation were studied. The comprehensive performance of the nozzle before and after ablation was calculated to evaluate the influence of ablation on motor performance. It was found that the profile ablation changed the shock wave distribution in the flow field of the expansion section, and the appearance of the ablation step also led to a new internal shock wave. The gas was compressed, and the flow direction, velocity, pressure, temperature, etc., were changed. After the ablation, the temperature and pressure of the nozzle outlet location increased, the velocity decreased, and the total thrust and specific impulse decreased.