bluuebunny/crossref_metadata_2025 huggingface dataset:
Tin Oxide/Nitrogen‐Doped Graphene Quantum Dots Composite Nanotubes: An Efficient Electrode for Supercapacitors
Authors: Ch Seshendra Reddy , Bulakhe Ravindra N. , Modigunta Jeevan Kumar Reddy , Murali G. , A. Reddy Sivasankar , Kim Jiyeong , Park Eunji , Kang Byungin , In Insik , Roy Arup Kumer , Ramakrishnan Kiruthiga , Rednam Udayabhaskar
Published: July 2022

Tin oxide (SnO2) and nitrogen‐doped graphene quantum dots (N‐GQDs) composite nanotubes (SnO2/N‐GQD NTs) were fabricated by the electrospinning technique and followed by the thermal annealing method for the application in supercapacitor as an electrode. SnO2/N‐GQD NTs with different ratio of N‐GQDs were prepared by adding different ratios of N‐GQDs along with tin chloride during the electrospinning process. The prepared composite's structure and morphological properties were characterized by using different techniques like XRD, FE‐SEM, TEM, and XPS. The supercapacitor performance of the SnO2/N‐GQD NTs composite was analyzed by the electrochemical studies such as cyclic voltammetry and galvanostatic charge‐discharge (GCD) measurement in 2 M KOH solution as electrolyte. The electrochemical analyses of SnO2/N‐GQD NTs was tested at different scan rates and current densities. SnO2/N‐GQD NTs prepared using 3 wt.% of N‐GQDs showed an excellent capacity retention even after 5000 GCD cycles and exhibited a maximum specific capacitance of 420 mF g-1 at a current density of 8 mA cm-2 in comparison to pure SnO2 NTs (230 mF cm-2).
Serialized Recommendation Technology Based on Deep Neural Network
Authors: Jin Long
Published: July 2022

Since the construction of brain network is like organic brain organization, profound brain network has high effectiveness and high accuracy in separating data from profound elements, fit for multifacet learning, conceptual component portrayal, cross-space learning capacity, multisource, heterogeneous data content. This paper presents brain network models, multifacet perceptrons, RBM models, and convolutional brain organizations. In this paper, the experimental design is carried out according to the principle of the model. Compared with the traditional commonly used CMF, PMF, SVD, and item-based algorithms, the proposed two models have lower root mean square error and higher recall rate. The trial brought about by this paper shows that through investigation, it is trusted that because of the impact of the component of the info information, too many secret layers might prompt overfitting. Accordingly, this paper sets the quantity of stowed away layers in the DNN organization to 2.
The Regulatory Role of Ferroptosis in Bone Homeostasis
Authors: Xiong Yuan , Chen Lang , Lin Ze , Hu Yiqiang , Panayi Adriana C. , Zhou Wu , Sun Yun , Cao Faqi , Liu Guodong , Dai Guangdong , Mi Bobin , Liu Guohui
Published: July 2022

Ferroptosis is an iron-dependent form of programmed cell death and an important type of biological catabolism. Through the action of divalent iron or ester oxygenase, ferroptosis can induce lipid peroxidation and cell death, regulating a variety of physiological processes. The role of ferroptosis in the modulation of bone homeostasis is a significant topic of interest. Herein, we review and discuss recent studies exploring the mechanisms and functions of ferroptosis in different bone-related cells, including mesenchymal stem cells, osteoblasts, osteoclasts, and osteocytes. The association between ferroptosis and disorders of bone homeostasis is also explored in this review. Overall, we aim to provide a detailed overview of ferroptosis, summarizing recent understanding on its role in regulation of bone physiology and bone disease pathogenesis.
Research on Application of 3D Simulation Technology in Industrial Product Design Technology
Authors: Huang Chenhan , Shi Daijiao
Published: July 2022

In order to study the driving effect of industrial product design, a method based on the application of 3D simulation technology in industrial product design technology was proposed. This method introduces the information about the change in industrial product design in industrial enterprises and analyzes the application of 3D simulation technology in industrial product design by taking DIALux, industrial robot, and resource information search system as examples. The results show that the application of 3D simulation system needs to be combined with industrial software, and the development of industrial software business mainly based on 3D simulation technology is emphasized so that the business revenue of enterprises increases from 709 million yuan in 2029 to 1.385 billion yuan in 2021, with a compound growth rate of 25.01%, which has achieved good economic benefits. 3D simulation technology plays an important role in promoting the development of industrial product design technology. It is necessary to actively promote the integration between 3D simulation technology and industrial software.
Audience Dissemination Response Law and Action Mechanism Based on Internet Rumor Computer Big Data
Authors: Jiang Jingjing , Li Yao , Yu Wenwu , Li Nan , Tao Xiaoxia
Published: July 2022

Various novel Internet rumors have also emerged. This article mainly researches the audience dissemination response law and the mechanism of action. This paper constructs a model of network rumor spreading. In the homogeneous network, we know that for the rumors that the initial infection rate is large, the increase caused by the herd phenomenon is not very large. For heterogeneous networks, we need to separately examine the increase of nodes and sparse nodes in the network under different initial infection rates. With the change of time, the number of nodes in each state will change due to interaction. When an ignorant person contacts a communicator, the ignorant person will be converted into a rumor communicator with a certain probability. Affected by the memory ability of individual user nodes, each online rumor support node and online rumor opposition node becomes an online ordinary user node with a constant probability f. Affected by the rumor support node, an online ordinary user node is transformed into a receiving unread node by a rumor support node with probability β at time t. Experimental data shows that when the herd effect is not considered, it is found that the transmission peak is 0.4328 at t = 10. When the herd effect is introduced, the spread of rumors reaches the transmission peak 0.5431 at t = 8. After the introduction of the herd effect, the peak value increased by 25.5%, and the time to reach the peak was shortened by 20%. The results show that computer big data technology has a significant inhibitory effect on the spread of Internet rumors.
Identification of a Seven-Differentially Expressed Gene-Based Recurrence-Free Survival Model for Melanoma Patients
Authors: Dong Yong , Miao Qian , Li Da
Published: July 2022

Melanoma is a malignant tumor that originates in melanocytes of the skin or mucous membrane, which has a high mortality rate and worse prognosis. Therefore, perspective prognosis evaluation seems more important for patients’ treatment. Gene expression profiles of melanoma were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, respectively. 130 consistent differentially expressed genes (DEGs) were identified between melanoma and nevus tissues from two GEO cohorts. Prognostic genes were identified by univariate analysis, and 20 of them were regarded to be associated with the recurrence-free survival (RFS) of melanoma patients. Then, the LASSO Cox regression analysis chose seven of them to establish a seven-DEG-based RFS predicting signature. We demonstrated that this model was more powerful to predict RFS risk than other individual clinical features and was able to independently predict the RFS outcomes in different subsets of patients. We attempted to search for the underlying mechanisms by analyzing the coexpression genes of the seven candidates, and the pathway enrichment analyses indicated that immune response-related pathways might play a critical role in melanoma progression. Finally, we establish a robust seven-DEG-based RFS predicting signature, which will facilitate the personalized treatment of melanoma patients.
Improvement of Speech Recognition Technology in Piano Music Scene Based on Deep Learning of Internet of Things
Authors: Bai Jie
Published: July 2022

The main goal of speech recognition technology is to use computers to convert human analog speech signals into computer-generated signals, such as behavior patterns or binary codes. Different from speaker identification and speaker confirmation, the latter attempts to identify or confirm the speaker who uttered the speech rather than the lexical content contained in it. The short-term idea is that it should be able to record the musical sound played by the user with a certain musical instrument, then extract the note and duration information from it, and finally generate the corresponding MID file according to the MIDI standard, which can set the type of musical instrument in advance to complete the function of musical sound transformation, such as playing with a harmonica, and playing the MID at the end is the piano sound. With the rapid development of the mobile Internet, fields such as machine learning, electronic communication, and navigation have placed high demands on real-time and standard text recognition technology. This paper merges the sound of visual music into text-based data set training, uses the exported scanner features for model training, uses the model to extract features, then uses the features for prior training, and then uses pretraining. DNN results show that the combined training of target prevention and expansion plans, by replacing long-term and short-term memory networks, end-to-end speech recognition programs, and behavioral tests organized by mobile devices, can provide a larger receptive field combined with expanded convolution instead of long and short periods. The experimental results show that when the input sampling point is 2400, it can be seen that the convergence speed of the model becomes slower with more than 90 iterations and the loss of the model on the verification set increases with the increase in the number of iterations. This shows that the model in this paper can fully meet the needs of speech recognition in piano music scenes.
Research on the Application of Blended Teaching Mode with “Deep Learning” Orientation
Authors: Zhao Jinting , Lu Jing , Zou Xiang , Zhu Zhanglong
Published: July 2022

When combined with deep learning theory and blended learning characteristics, the author develops a blended teaching mode centered on “deep learning.” A blended teaching mode centered on “deep learning” is developed, and the professional course of “Personal Finance” at a higher vocational college in Jiangsu Province is used to demonstrate and apply the method. According to studies, teachers can effectively promote students’ deep learning by utilizing information teaching resources in a blended “online + offline” mode of instruction. The research shows that teachers can create a meaningful learning environment, stimulate students’ enthusiasm for autonomous learning, guide students to complete the construction of knowledge meaning, deepen the transmission and application of knowledge, solve practical problems, effectively promote students’ in-depth learning, and design a variety of evaluation methods to assist this process. The study of learning environment can stimulate students’ enthusiasm for autonomous learning, guide students to complete the construction of knowledge significance, strengthen knowledge transfer and application to practical problems, develop a variety of evaluation methods, and help students reflect on their learning gains.
Characteristics of HBV Novel Serum Markers across Distinct Phases in Treatment-Naïve Chronic HBV-Infected Patients
Authors: Liao Hao , Li Le , Zheng Wei V. , Zou Jun , Yu Guangxin , Si Lanlan , Ge Feilin , Zhou Tao , Ji Dong , Chen Xiangmei , Xu Dongping , Cheng Guanxun , Liu Yan , Chen Junhui
Published: July 2022

Background and aims. To investigate the clinical implications of serum HBV RNA, serum hepatitis B core-related antigen (HBcrAg), and quantitative anti-HBc in treatment-naïve patients with chronic HBV infection. Methods. A total of 111 patients in total from different disease phases were recruited, including 21 in immune-tolerant (IT) phase, 49 in immune-clearance (IC) phases, 29 in immune-control or low replicative (LR) phase, and 12 in reactivation phases. Serum HBV RNA, anti-HBc, HBcrAg, and intrahepatic covalently closed circular DNA (cccDNA) were quantified and each of these indicator’s correlation with liver inflammation was analyzed. Results. HBeAg-positive individuals had significant higher serum levels of HBV RNA and HBcrAg than those who were HBeAg negative, similar to that of serum HBV DNA. Comparatively, HBV RNA (

r

 =0.79,

P
<
0.01

) and HBcrAg (

r

 =0.78,

P
<
0.01

) had almost same higher overall correlation with the cccDNA, as that of HBV DNA (

r

 =0.81,

P
<
0.01

). Serum anti-HBc level (

r

 = -0.52,

P
<
0.05

) is negatively correlated with cccDNA level at IT phase rather than the other three phases. When set the cutoff value at 4.00 log10 IU/mL, serum anti-HBc showed potential to indicate liver inflammation, with AUC as 0.79 and the specificities as 78.85% for HBeAg positive, and with AUC as 0.72 and the specificities as 62.16% for HBeAg-negative patients, respectively. Conclusions. In treatment-naïve patients, levels of serological markers HBV RNA and HBcrAg could mirror intrahepatic cccDNA level, but were not superior to HBV DNA level. Serum anti-HBc level had certain potential to be used as a predicting marker for liver inflammation.
Discrete Dynamic Modeling Analysis of Data Mining Algorithm under the Background of Big Data in the Strategic Goal of Sustainable Development of College Physical Training
Authors: Li Yunong , Liu Shaohua
Published: July 2022

With the popularization of intelligent devices and the rapid development of information technology, many valuable data become easier to obtain. After obtaining massive data, the problem of how to make more efficient and scientific use of these data is in front of us. Data mining is a deeper processing of data. It is aimed at mining the internal associations hidden in complex data and making the data give full play to its maximum value. At present, college physical training is also in urgent need of more scientific and modern upgrading and reform. Data mining is introduced into college physical training so that physical training data is no longer just a simple collection, query, and storage. This paper uses and introduces the basic content of Apriori algorithm. The actual college sports training data are collected for preprocessing, and the discrete dynamic model is established by using Apriori algorithm. Using this model, the actual data are deeply analyzed. Using the strong correlation subintegration results, the physical training of college students is optimized and reformed. The scores of college students’ physical quality are improved, and the goal of sustainable development of college physical training is achieved. Based on the data of 126 students in four classes of a major in 2020, this paper mines the frequent subsets with strong correlation in the data and finds out the relationship between the correlation and trust between subsets. In the 70-day experiment, the scores of college students’ comprehensive physical quality have been significantly improved, which proves that the discrete dynamic model established by Apriori algorithm data mining has a significant role in college physical training. The questionnaire survey results show that 83% of the college students are very satisfied with the discrete dynamic model-assisted physical training, and 16% of the college students are basically satisfied with the system. Apriori algorithm has significant advantages in dealing with big data. Single Apriori algorithm also has some defects. The efficiency of data mining, data security, and the accuracy of model analysis results need to be tested in practice.
Swarm Intelligence Algorithms for Optimal Scheduling for Cloud-Based Fuzzy Systems
Authors: AlSuwaidan Lulwah , Khan Shakir , Almakki Riyad , Baig Abdul Rauf , Sarkar Partha , Ahmed Alaa E. S.
Published: July 2022

A fuzzy cloud resource scheduling model with time-cost constraints is built using fuzzy triangular numbers to represent uncertain task execution time. Task scheduling reduces total time and cost spent on a project. It connects virtual machines and functions. Particle swarm optimization (HPO) is used to plan cloud resources (HSOA). The approach uses orthogonal particle swarm initialization to increase the quality of the initial particle exploration, rerandomization to regulate the particle search range, and real-time updating of inertia weights to control particle speed. The suggested problem model and optimization approach are evaluated using random simulation data provided by the CloudSim simulation platform. Less overall execution time and a lower cost are shown to have fast convergence and solution capabilities in experiments.
[Retracted] Reset and Integration of Music Instructional Resources Using Deep Convolutional Neural Networks
Authors: Lei Huiling
Published: July 2022

In order to overcome the problem that learners and teachers cannot find instructional resources to meet their needs and information overload in the massive resources, this article proposes and designs a music instructional resource management platform based on DCNN. This article expounds the overall goal, design principle, overall structure, and interface design of the system. At the same time, the whole construction process of a music instructional resources integration system based on DCNN is discussed in detail from the aspects of configuration of development environment, localization of platform interface, and realization of main functions of the system. In addition, through the demand analysis tool, the demand of college music instructional resources management is analyzed in detail and deeply, and the demand document is formed. This article makes an in‐depth study on the categories of music instructional resources and summarizes the resource classification methods that are in line with the actual instructional activities. The experiments show that the accuracy of the proposed algorithm is improved by about 6% compared with the fuzzy clustering algorithm. At the same time, the stability of this system can reach 96.14%. This system is rich in functions and easy to use and can provide a feasible scheme for the management of instructional resources in various disciplines.
Application of an Artificial Intelligence System Recognition Based on the Deep Neural Network Algorithm
Authors: Zhang Yaru , Zhang Qian , Yang Jingxuan
Published: July 2022

With the development of artificial intelligence and computer technology, the deep neural network algorithm is applied to the intelligentization of various fields of production and life. However, from the current application status, the application of artificial intelligence technology has many shortcomings. Based on this, this paper starts with the deep neural network algorithm, takes face recognition as the research tool, and deeply studies how to use the deep neural network algorithm to demonstrate the application of intelligent face recognition in complex environments. A face recognition neural network algorithm is proposed, and the accuracy of the algorithm is checked by testing. The results show that the average accuracy of a single sample in the LFW dataset is 99.17%, and the efficiency of using a single sample is close to that of many smelting models, which can be applied to various intelligent recognition scenarios.
Partition KMNN-DBSCAN Algorithm and Its Application in Extraction of Rail Damage Data
Authors: Li Yujun , Yang Zhi , Jiao Shangbin , Li Yuxing
Published: July 2022

In order to realize intelligent identification of rail damage, this paper studies the extraction method of complete damage ultrasonic B-scan data based on the density-based spatial clustering of applications with noise algorithm (DBSCAN). Aiming at the problem that the traditional DBSCAN algorithm needs to manually set the Eps and Minpts parameters, a KMNN-DBSCAN (K-median nearest neighbor DBSCAN) algorithm is proposed. The algorithm first uses the dataset’s own distribution characteristics to generate a list of Eps and Minpts parameters and then determines the optimal Eps and Minpts through an optimization strategy to achieve complete self-adaptation of the two parameters of Eps and Minpts. In order to further improve the clustering performance of the algorithm, the partition idea is introduced, and the partition KMNN-DBSCAN algorithm is proposed to solve the problem that the clustering results of the DBSCAN algorithm are inconsistent with the actual categories on datasets with uneven density. The experimental results show that the KMNN-DBSCAN algorithm has higher clustering accuracy and silhouette coefficient (SC) for the D037 dataset ultrasound information group (UIG) division; compared with the KMNN-DBSCAN algorithm, the proposed partition KMNN-DBSCAN algorithm has higher clustering accuracy, F-Measure, and SC values. The partition KMNN-DBSCAN algorithm achieves accurate division of all damage UIG on the damaged B-scan data with large density differences, and completes the effective extraction of complete damage data.
Association of Toll-Like Receptor 2 (TLR2) Expression in Placenta and Intrauterine Exposure to Hepatitis B Virus
Authors: Utami Dian , Chalid Maisuri T. , Masadah Rina , Sjahril Rizalinda , Bahagia Febriani Andi Dwi
Published: July 2022

Introduction. The placenta is a specialized organ that only performs during pregnancy and serves as an immunological barrier in preventing pathogens to reach the fetus. It has been known that toll-like receptors (TLRs) on the placenta respond to antigens, such as zymosan, lipopolysaccharide, and other viral infections. This study analyzes the distribution of TLR2 protein and hepatitis B DNA virus (HBV DNA) virus identification to gain an understanding of hepatitis B viral transmission from the mother to child. Methods. We performed enzyme-linked immunoadsorbent assay of HBeAg, HBsAg titer, Anti-HBs, and Anti-HBc to 59 HBsAg-positive pregnant women and identified HBV DNA using nested PCR in their cord blood during delivery for evidence of exposure to HBV DNA. The expression of TLR2 protein in the placenta was performed using immunohistochemistry analysis. Results. Intrauterine exposure to the hepatitis B virus occurred in 69.5% of all pregnant women who were HbsAg positive. TLR2 expression was predominantly identified in syncytiotrophoblast and cytotrophoblast cells with the highest score in mothers aged 20–35 years (75%), multigravida (58.3%), and mothers with term pregnancies (70.8%). Statistical analysis results showed that placental TLR2 expression did not indicate any association with hepatitis B virus DNA identified in cord blood with a p value of 0.730 and an OR of 0.650 (95% CI 0.173–2.440). Conclusion. TLR2 expression is not associated with intrauterine exposure of hepatitis B virus.
Prediction of Urban Scale Expansion Based on Genetic Algorithm Optimized Neural Network Model
Authors: Kuang Hewu
Published: July 2022

With the continuous development of urbanization, the urban population is becoming more and more dense, and the demand for land is becoming more and more tense. Urban expansion has become an indispensable part of urban development. This paper studies the optimization of neural network structure by genetic algorithm, puts forward the prediction model of urban scale expansion based on a genetic algorithm optimization neural network, and compares the performance of the model with the basic model. A genetic algorithm BP neural network (GA-BP) optimized by the genetic algorithm is used to shorten the running time of the algorithm and improve the prediction accuracy, but it is easy to fall into local solution. The genetic algorithm is improved by immune cloning algorithm, and the CGA-BP neural network model is established to obtain the global optimal solution. Compared with the BP neural network model and GA-BP neural network model, the CGA-BP neural network model converges faster, and the training times reach the error condition after 79 times, while the BP neural network model and GA-BP neural network model need 117 times and 100 times, respectively, and the fitness value corresponding to the number of iterations of the model is larger. Therefore, the CGA-BP neural network algorithm can make prediction more accurately and quickly and predict the expansion of urban scale through urban conditions.
[Retracted] Apoptotic and Antioxidant Activity of Gold Nanoparticles Synthesized Using Marine Brown Seaweed: An In Vitro Study
Authors: Rajeshkumar S. , Parameswari R. P. , Jayapriya J. , Tharani M. , Ali Huma , Aljarba Nada H. , Alkahtani Saad , Alarifi Saud
Published: July 2022

A major paradigm shift in the field of nanobiotechnology is the invention of an eco‐friendly, economical, and green approach for synthesis of metal nanoparticles. In the present study, we have synthesized gold nanoparticles (AuNPs) using aqueous extracts of marine brown seaweed Sargassum longifolium. The synthesized nanoparticle was subjected to characterization using different techniques such as UV‐Vis spectroscopy, Fourier transform infrared spectroscopy, atomic force microscope, scanning electron microscope, transmission electron microscope, and elemental dispersive X‐ray diffraction. Further, the seaweed extract and the synthesized AuNPs were evaluated for its anticancer effect using MG‐63 human osteosarcoma cells besides in vitro antioxidant effect. The formation of S. longifolium‐mediated synthesis of gold nanoparticles was demonstrated by UV‐Vis spectroscopy. Presence of elemental gold was confirmed by EDX analysis. TEM analysis demonstrated spherical morphology of the synthesized AuNPs and SEM analysis revealed the particle size to be in the range of 10‐60 nm. The FTIR showed the presence of hydroxyl functional groups. The toxicity of S. longifolium extract and the synthesized AuNPs was tested using brine shrimp lethality assay at different concentrations with results showing both seaweed extract and AuNPs to be nontoxic. Both S. longifolium and AuNPs exhibited significant antioxidant activity by scavenging DPPH free radicals and H2O2 radicals. Significant antiproliferative effect was observed against MG‐63 osteosarcoma cells. It was also shown that the seaweed extract and the AuNPs induced cytotoxicity in cell lines by mechanism of apoptosis. In conclusion, this study provided insight on AuNPs synthesized from S. longifolium as a potent antioxidant and anticancer agent.
Deconstruction of Road Logistics Transportation Cost Management Evaluation Based on Optimal Solution of Linear Programming
Authors: Liu Shu
Published: July 2022

Due to the rapid development of the logistics industry, the business volume of the road transportation industry is also growing rapidly and its transportation costs are also increasing. Therefore, it is particularly important to control the transportation cost. This paper aims to evaluate and analyze the cost management of road logistics transportation based on the optimal solution of linear programming. This paper introduces the basic concept of highway logistics in general and analyzes its components and characteristics. Road logistics transportation has the characteristics of fast speed, low investment, strong flexibility, and easy to adapt to local conditions and does not require much infrastructure. We then analyzed the general linear programming model, made a brief introduction to its mathematical model, and proposed the construction of the optimization model to explore the transportation model of production and sales balance. Finally, taking W logistics company as a case to analyze and discuss, the influence of the transportation route in transit on the transportation cost is studied. And its structure and the corresponding transportation cost of logistics enterprises are analyzed in detail. The experimental results of this paper show that by comparing the calculation results of the road segment method and the linear programming model, the total freight calculated by the segment method is 5610 yuan, while the total freight calculated by the linear programming method is 5490 yuan. Comparing the piecewise methods, it can be found that the linear programming method is more economical in transportation cost than the piecewise method. Therefore, when logistics companies optimize road transportation costs, it is more feasible and effective to use linear programming methods.
Inhibition of SphK1/S1P Signaling Pathway Alleviates Fibrosis and Inflammation of Rat Myocardium after Myocardial Infarction
Authors: Wu Xiaokui , Xu Junwei , Li Xiangyu , Dai Jian , Wang Linlin
Published: July 2022

Objective. The sphingosine kinase 1 (SphK1)/sphingosine-1-phosphate (S1P) signaling pathway is involved in fibrosis and inflammatory responses of myocardial tissue after myocardial infarction (MI). The purpose of our study was to explore the role of SphK1/S1P signaling pathway in myocardial injury after MI. Materials and Methods. We used Sprague-Dawley (SD) rats to make MI models and detected the changes of SphK1 and S1P in rats at 1, 7, and 14 days after MI. SphK1 inhibitor PF543 was used to treat MI rats, and we detected the changes in myocardial function and structure in rats by cardiac function test, 2,3,5-triphenyl tetrazolium staining, and histological staining. In addition, we used H2O2 to induce H9c2 cell injury to investigate the effect of PF543 on the viability of myocardial cells. Results. Myocardial tissue lesions and fibrosis were observed at 7 and 14 days after MI, and the expressions of SphK1 and S1P in the injured myocardial tissues increased significantly in day 7 and day 14 in comparison to the control group. After treatment of MI rats with PF543, the structure of rat myocardial tissue was significantly improved and the degree of fibrosis was reduced. After MI, the expression of α-SMA and collagen I in the myocardium of rats was significantly increased while PF543 decreased their expression. PF543 also improved the cardiac function of MI rats and reduced the expression of IL-1β, IL-6, and TNF-α in the serum. PF543 also increased the viability of H9c2 cells in vitro. Conclusions. The inhibition of the SphK1/S1P signaling pathway contributed to the relief of myocardial injury in MI rats. PF543 improved the myocardial structure and function of MI rats and reduced the level of fibrosis and inflammation in MI rats.
Design and Application of a Financial Distress Early Warning Model Based on Data Reasoning and Pattern Recognition
Authors: Hu Xiaoya
Published: July 2022

Since the 1990s, emerging market financial crises have occurred frequently, causing huge damage to the real economy, and if we cannot find effective means of early warning and prevention of financial crises, the entire international economy and society will bear the high costs of crisis management. Difference nonparametric test and Spearman nonparametric correlation analysis were carried out with cash flow financial data, and 14 financial indicators with strong discriminant ability were selected from 28 financial indicators as the input variables of the model. Due to the limitations of traditional statistical methods, a BP neural network financial distress early warning model is established. Finally, a particle swarm optimization BP neural network financial early warning model is established for the shortcomings of the BP network. These 14 indicators can have strong information timeliness. The prediction accuracy rates of the two early warning models for the test samples are 80% and 85%, respectively. The empirical results show that the two models have good prediction effects. The prediction effect of the swarm optimization BP neural network model is better than that of the BP neural network model. Therefore, the particle swarm optimization BP neural network model proposed in this paper is suitable for solving the problem of discrimination and prediction of the financial distress of enterprises. The company’s financial distress early warning has good application prospects and application value. Therefore, it has very important research significance for the early warning of the corporate financial crisis.
Analysis and Research on the Impact of Physical Exercise on Residents’ Health Based on the Improved BP Neural Network Model
Authors: Wang Dehui , Liu Chao , Zhou Chong , Liang Jiling
Published: July 2022

With the rapid improvement of social economy and the enhancement of people’s health awareness, it is necessary to make an in‐depth analysis of the rationality of physical exercise and the physical quality of residents. Hence, this study aims to explore the algorithm optimization of the improved BP model to analyze the effect of exercise intervention on improving public sports effect. K‐clustering and Levenberg–Marquardt algorithm were used to construct an improved BP neural network model to determine the sample clustering center, as well as the weight and threshold of the indicators, so as to optimize the analysis algorithm of improving public sports effect. MATLAB simulation shows that under the target error conditions of 0.01, 0.005, 0.001, and 0.0001, the target error rate and iteration times of the improved BP model are better than the standard BP model, and the time consumption is shorter, which can be conducive to more accurately analyzing the changes of improving public sports effect under exercise intervention. Therefore, the improved BP model can effectively solve the problems of data clustering and result error rate adjustment in the process of improving public sports effect analysis and improve the analysis speed and accuracy.
Research on Fake News Detection Based on Diffusion Growth Rate
Authors: Chen Jinyin , Jia Chengyu , Li Qinfeng , Zheng Haibin , Zhao Wenhong , Yan Mingyuan , Lin Changting
Published: July 2022

With the rapid development of the Internet, social media has become a convenient online platform for users to obtain information, express opinions, and communicate with each other. Users are keen to participate in discussions on hot topics and exchange opinions on social media. A lot of fake news has also arisen at this moment. However, existing fake news detection methods have the problem of relying too much on textual features. Textual features are easy to be tampered with and deceive the detector; thus, it is difficult to distinguish fake news only by relying on textual features. To address the challenge, we propose a fake news detection method based on the diffusion growth rate (Delta-G). To identify the real and fake news, Delta-G uses graph convolutional networks to extract the diffusion structure features and then adopts the long-short-term memory networks to extract the growth rate features on time series. In the experiments, Delta-G is verified on two news datasets, Twitter and Weibo. Compared with the three detection methods of decision tree classifier, support vector machines with a propagation tree kernel, and RvNN, the accuracy of the Delta-G on the two datasets is improved by an average of 5% or more, which is better than all the baselines.
Design of International Chinese Education Promotion Platform Based on Artificial Intelligence and Facial Recognition Technology
Authors: Shen Yi , Sun Sixian
Published: July 2022

With the continuous development of today’s society, digital image processing technology has been applied in more and more fields, among which authentication in digital image processing technology has become a hot field. In the process of identity verification, the face is used as the basis of feature recognition because the method of using the face as a feature basis is more easily accepted by the public and the operation is simple and the feasibility is stronger. In the online education model, observing and comparing students’ facial emotions through the platform and analyzing students’ learning goals, learning effects, learning emotions, and contradictions and conflicts arising in the process of cooperation have become an effective teaching evaluation system. Up to now, China has developed into the second largest economy in the world. The global “Chinese fever” has brought China’s culture into a new stage of development. Countries in the world learn Chinese culture by developing Chinese language courses. By building a Chinese learning intelligent system with a B/S structure, this system can effectively evaluate the teaching process. It can be seen from the test results that the platform meets the basic requirements of functional design.
Progressive Distributed and Parallel Similarity Retrieval of Large CT Image Sequences in Mobile Telemedicine Networks
Authors: Zhuang Yi , Jiang Nan , Xu Yongming
Published: July 2022

Computed tomography image (CTI) sequence is essentially a time-series data that typically consists of a large amount of nearby and similar CTIs. Due to the high communication and computational costs, it is difficult to perform a progressive distributed similarity retrieval of the large CTI sequence (CTIS)s, particularly in resource-constraint mobile telemedicine network (MTN)s. In this paper, we present a Dprs method—progressive distributed and parallel similarity retrieval scheme for the CTISs in the MTN. To the best of our knowledge, there is little research on the Dprs processing, especially in the MTN. Four supporting techniques (i.e., (1) PCTI-based similarity measurement, (2) lightweight privacy-preserving strategy, (3) SSL-based data distribution scheme, and (4) the UDI framework) are developed. The experimental evaluation indicates that our proposed Dprs method is more progressive than the state of the art, with a significant reduction in response time.
[Retracted] Risk Factors and Treatments of Suprachoroidal Hemorrhage
Authors: Jiang Huaiyan , Gao Yang , Fu Wenzhong , Xu Hongxia
Published: July 2022

Suprachoroidal hemorrhage (SCH) is a rare but serious sight‐threatening complication of inner eye surgery. Despite continuous advances in treatment, visual prognosis remains poor. The disease has a more typical clinical presentation, the etiology and pathogenesis are not well defined, and intraoperative ocular and systemic factors may induce fulminant SCH. To investigate risk factors and treatments of SCH‐associated intraocular surgeries, summarize diagnosis, characteristics, management, and prevention of SCH developed during and after intraocular surgeries. A retrospective study of SCH occurred in six cases of intraocular surgeries including cataract, glaucoma, pars plana vitrectomy (PPV), and silicone oil removal surgery. Assess baseline systemic and ocular characteristics of SCH eyes. Analyze the second surgery timing and technique, and visual outcomes were measured. SCH occurred in six patients including five eyes during surgeries and one eye after the surgery. Three eyes that underwent cataract surgery had hard nucleuses (nuclear sclerotic 4+). One eye was due to hypotony during the vitrectomy procedure. One eye developed SCH when silicone oil was extracted from the eyeball. One eye developed delayed SCH after glaucoma surgery. Incision closure and anterior chamber deepening were performed. B‐scan ultrasonography was used to diagnose SCH, and determine the timing and location of sclerotomy for the second surgery. Vitrectomy and sclerotomy were performed in five eyes. The median follow‐up time was six months. The final best‐corrected visual acuity (BCVA) was 0.3 in one eye, one eye had light perception with retinal adherence, and four eyes had no light perception with retinal detachment. The results showed that risk factors including advanced age, hypertension, taking anticoagulants, antiplatelet drugs, and cardiovascular drugs were systemic risk factors, and hard nucleus (nuclear sclerosis 4 +) cataract, long‐term uncontrolled ocular hypertension glaucoma, vitrectomy, silicone oil removal, high myopia, aphakia, previous intraocular surgery, intraocular pressure during surgery, and others were ocular risk factors. The most important risk factor is a sudden drop in intraocular pressure during or after surgery. The outcome of visual acuity depends on retinal status. Because of the poor prognosis, the prevention of SCH is of utmost importance during intraocular surgery.
Semaglutide Protects against 6-OHDA Toxicity by Enhancing Autophagy and Inhibiting Oxidative Stress
Authors: Liu Dong-xing , Zhao Chen-sheng , Wei Xiao-na , Ma Yi-peng , Wu Jian-kun
Published: July 2022

Parkinson’s disease (PD) is the second most prevalent neurodegenerative disorder for which no effective treatment is available. Studies have demonstrated that improving insulin resistance in type 2 diabetes mellitus (T2DM) can benefit patients with PD. In addition, a neuroprotective effect of glucagon-like peptide-1 (GLP-1) receptor agonists was demonstrated in experimental models of PD. In addition, there are some clinical trials to study the neuroprotective effect of GLP-1 analog on PD patients. Semaglutide is a long-acting, once-a-week injection treatment and the only available oral form of GLP-1 analog. In the present study, we treated the human neuroblastoma SH-SY5Y cell line with 6-hydroxydopamine (6-OHDA) as a PD in vitro model to explore the neuroprotective effects and potential mechanisms of semaglutide to protect against PD. Moreover, we compared the effect of semaglutide with liraglutide given at the same dose. We demonstrated that both semaglutide and liraglutide protect against 6-OHDA cytotoxicity by increasing autophagy flux and decreasing oxidative stress as well as mitochondrial dysfunction in SH-SY5Y cells. Moreover, by comparing the neuroprotective effects of semaglutide and liraglutide on PD cell models at the same dose, we found that semaglutide was superior to liraglutide for most parameters measured. Our results indicate that semaglutide, the new long-acting and only oral GLP-1 analog, may be represent a promising treatment for PD.
Preoperative Body Composition Combined with Tumor Metabolism Analysis by PET/CT Is Associated with Disease‐Free Survival in Patients with NSCLC
Authors: Tan Hongpei , Ma Mengtian , Huang Jing , Dong Yuqian , Liu Jiahao , Mi Ze , Zheng Kai , Hu Shuo , Rong Pengfei
Published: July 2022

Objective. To evaluate the relationship between preoperative primary tumor metabolism and body composition in patients with NSCLC and analyze their effects on DFS. Method. A retrospective study was conducted on 154 patients with NSCLC. All patients were scanned by baseline 18F‐FDG PET/CT. SUVmax (maximum standard uptake value) of primary tumor, liver SUVmean (mean standard uptake value), and spleen SUVmean were measured by AW workstation. The skeletal muscle area (SMA), skeletal muscle mass index (SMI), skeletal muscle radiation density (SMD), visceral fat area (VFA), visceral adipose tissue index (VATI), and skeletal muscle visceral fat ratio (SVR) were measured by ImageJ software. Kaplan–Meier survival analysis was used to evaluate the impact of the above parameters on DFS. Results. Compared with the low SUVmax group of primary tumors, the mean values of SMA, VFA, and VATI in the high SUVmax group were significantly higher. In addition, there were obvious differences in histopathological type, pathological differentiation, AJCC stage, and T stage between the two groups. Univariate analysis of DFS showed that VFA, VATI, pathological differentiation, tumor SUVmax, AJCC stage, tumor T stage, and N stage all affected the DFS of patients except for the parameters reflecting skeletal muscle content. Multivariate regression analysis showed that only VFA and SUVmax were associated with DFS. Kaplan–Meier survival analysis showed that high SUVmax, low VFA, high T stage, and high N stage were related to the decrease of DFS. Conclusion：Preoperative 18F‐FDG PET/CT could comprehensively evaluate the primary tumor SUVmax, skeletal muscle, and visceral fat in patients with NSCLC. The combination of primary tumor SUVmax and visceral fat area can well evaluate the prognosis of patients with NSCLC.
Angiotensin Receptor Blocker is Associated with a Lower Fracture Risk: An Updated Systematic Review and Meta-Analysis
Authors: Wu Jing , Wang Mei , Guo Man , Du Xin-Yi , Tan Xiao-Zhen , Teng Fang-Yuan , Xu Yong
Published: July 2022

Background. Angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) are widely used in the treatment of hypertension. Hypertension is often accompanied by osteoporosis. However, the relationship between ACEI/ARB and fractures remains controversial. The purpose of this meta-analysis was to update the potential relationship between ACEI/ARB and fractures. Methods. This meta-analysis was identified through PubMed, EMBASE, Cochrane Library, and Web of Science. Related studies about ACEI/ARB with the risk of fracture were published from inception to June 2022. Results. Nine qualified prospective designed studies, involving 3,649,785 subjects, were included in this analysis. Overall, the RRs of ACEI compared with the nonusers were 0.98 (95% CI: 0.88, 1.10;

P
<
0.001

) for composite fractures and 0.96 (95% CI: 0.87, 1.05;

P
=
0.048

) for hip fractures; the RRs of ARB compared to the nonusers were 0.82 (95% CI: 0.73, 0.91;

P
<
0.001

) for composite fractures and 0.85 (95% CI: 0.74, 0.97;

P
=
0.028

) for hip fractures. Furthermore, in the subgroup analysis, male may benefit from ARB (RR = 0.65, 95% CI: 0.49, 0.89,

P
=
0.028

), and the European may also benefit from ARB (RR = 0.86, 95% CI: 0.80, 0.93,

P
=
0.015

). Conclusions. ACEI usage will not decrease the risk of osteoporosis fracture. On the contrary, ARB usage can decrease the risk of total fracture and hip fracture, especially for males and Europeans. Compared with ACEI, for patients at higher risk of fracture in cardiovascular diseases such as hypertension, the protective effect of ARB should be considered.
A Generative Adversarial Network Based a Rolling Bearing Data Generation Method Towards Fault Diagnosis
Authors: Huo Lin , Qi Huanchao , Fei Simiao , Guan Cong , Li Ji
Published: July 2022

As a new generative model, the generative adversarial network (GAN) has great potential in the accuracy and efficiency of generating pseudoreal data. Nowadays, bearing fault diagnosis based on machine learning usually needs sufficient data. If enough near-real data can be generated in the case of insufficient samples in the actual operating condition, the effect of fault diagnosis will be greatly improved. In this study, a new rolling bearing data generation method based on the generative adversarial network (GAN) is proposed, which can be trained adversarially and jointly via a learned embedding, and applied to solve fault diagnosis problems with insufficient data. By analyzing the time-domain characteristics of rolling bearing life cycle monitoring data in actual working conditions, the operation data are divided into three periods, and the construction and training of the generative adversarial network model are carried out. Data generated by adversarial are compared with the real data in the time domain and frequency domain, respectively, and the similarity between the generated data and the real data is verified.