bluuebunny/crossref_metadata_2025 huggingface dataset:
MiR-299-3p Inhibits Nasopharyngeal Carcinoma Cell Proliferation and Migration by Targeting MMP-2
Authors: Tang Liang , Xu Mengdi , Zhu Huangfeng , Peng Yanlai
Published: August 2022

Purpose. Nasopharyngeal carcinoma (NPC) is a type of squamous cell carcinoma that originated from the epithelial cells of the nose and throat, and its incidence ranks the first among head and neck tumors. However, NPC has a unique and complex etiology that is not completely understood. MiR-299-3p was discovered to be abnormally expressed in cancers. However, the involvement of miR-299-3p in the incidence and progression of nasopharyngeal cancer remains unknown. Methods. The miR-299-3p expression in nasopharyngeal cancer samples and cell lines was identified using quantitative PCR (qPCR). Nasopharyngeal cancer cells were evaluated for proliferation, migration, and invasion using MTT, colony formation assay, and Transwell invasion assay. MiRBase and TargetScan databases identified the possible miR-299-3p target genes that were confirmed using a dual-luciferase reporter analysis. Additionally, the miR-299-3p target genes were validated by Western blot, colony formation assay, and Transwell assays. Results. It was found that miR-299-3p expression was low in nasopharyngeal cancer tissues and cell lines, according to qPCR data. Cell proliferation, colony formation, and migration were considerably reduced by miR-299-3p overexpression. Furthermore, matrix metalloproteinase 2 (MMP-2) expression was regulated by miR-299-3p, whereas MMP-2 knockdown significantly inhibited the capacity of nasopharyngeal cancer cells to form colonies and migrate. Overexpression of MMP-2 substantially reduced the miR-299-3p inhibitory impact on nasopharyngeal cancer cell migration and colony formation. Conclusion. The miR-299-3p acts as a tumor suppressor gene to suppress the growth and spread of nasopharyngeal cancer by regulating MMP-2 expression. Therefore, miR-299-3p and MMP-2 could be important therapeutic targets for suppressing nasopharyngeal cancer growth and metastasis.
Effect of Cognitive Behavioral Therapy on Stress Disorder, Cognitive Function, Motor Function, and Daily Living Ability of Patients with a Traumatic Brain Injury
Authors: Sun Meng , Zhuang Li
Published: August 2022

Purpose. The aim of the study is to observe the effects of cognitive behavioral therapy on stress disorder, cognitive function, motor function, and daily living ability of traumatic brain injury (TBI) patients. Methods. 84 patients with TBI admitted to our hospital from June 2019 to May 2021 were selected as the research subjects. They were divided into a control group (from June 2019 to May 2020) and an observation group (from June 2020 to May 2021), with 42 cases in each group. The control group received routine intervention; the observation group received cognitive behavioral therapy on the basis of the control group. Before and after intervention, the posttraumatic stress disorder (PTSD), cognitive function, motor function, and daily living ability of the two groups were observed. Results. After intervention, the PTSD-self-rating scale (PTSD-SS) scores of both groups were lower than those before intervention, and the PTSD-SS scores of the observation group were lower than those of the control group

P
<
0.05

. After intervention, the scores of the Montreal cognitive assessment (MoCA) scale, Fugl-Meyer assessment (FMA), and modified Barthel index (MBI) in both groups were higher than those before intervention, and the scores of MoCA, FMA, and MBI in the observation group were higher than those in the control group

P
<
0.05

. Conclusion. The application of cognitive behavioral therapy to TBI patients is beneficial to reduce the degree of PTSD and improve cognitive function, motor function, and daily living ability, which is worthy of clinical application.
Participation of Grid-Connected Energy Hubs and Energy Distribution Companies in the Day-Ahead Energy Wholesale and Retail Markets Constrained to Network Operation Indices
Authors: Kohansal Omid , Zadehbagheri Mahmoud , Kiani Mohammad Javad , Nejatian Samad
Published: August 2022

In this paper, the optimal scheduling of energy grids and networked energy hubs based on their participation in the day-ahead energy wholesale and retail markets is presented. The problem is formulated as a bilevel model. Its upper level minimizes the expected energy cost of electricity, gas, and heating grids, especially in the form of private distribution companies in the mentioned markets, in the first objective function, and it minimizes the expected energy loss of these networks in the second objective function. This problem is constrained by linearized optimal power flow equations. The lower-level formulation minimizes the expected energy cost of hubs (equal to the difference between sell and purchase of energy) as an objective function in the retail market. Constraints of this model are the operation formulation of sources and active loads and the flexibility limit of hubs. The unscented transformation approach models the uncertainties of load, renewable power, energy price, and energy demand of mobile storage. Then, the Karush–Kuhn–Tucker approach and Pareto optimization technique based on ε-constraint are adopted to extract the single-level single-objective formulation. Finally, obtained results verify the capability of the present method in improving the economic status of hubs and the economic and operation situation of the mentioned networks simultaneously so that the proposed scheme by managing the power of energy hubs compared with power flow studies has been able to reduce operating costs by 8%, reduce energy losses by 10%, and improve voltage profile and temperature by 36% and 30%.
LINC00887 Fosters Development of Clear Cell Renal Cell Carcinoma via Inhibiting CD8+ T Cell Immune Infiltration
Authors: Wu Jinfeng , Lin Rongcheng , Zhang Lei , Wei Yongbao , Zhang Ruochen , Cai Wanghai , Hu Weilie
Published: August 2022

Background. lncRNAs affect adaptive and innate immunity of cancer via mediating functional states of immune cells, genes, and pathways. Nonetheless, little is known about the molecular mechanism of lncRNA-mediated CD8+ T cell immune infiltration in progression of clear cell renal cell carcinoma (ccRCC). We designed this work to investigate the role of LINC00887 in regulating CD8+ T cell immune infiltration in ccRCC. Methods. Correlation between LINC00887 and immune factors and the expression level of LINC00887 in ccRCC were analyzed by bioinformatics methods (TCGA-KIRC database, “edgeR” package, “clusterProfiler” package, and “CIBERSORT” package). LINC00887 expression in ccRCC was examined via RT-qPCR. The cytokilling capacity of CD8+ T cells was evaluated by the lactate dehydrogenase assay. The apoptotic ability of CD8+ T cells was measured by flow cytometry. The chemotactic ability of CD8+ T cells was revealed by chemotaxis assay. CXCR3, CXCL9, and CXCL10 levels were assessed by RT-qPCR. Results. As suggested by bioinformatics analysis, LINC00887 was markedly upregulated in ccRCC patients and associated with expression of immune-suppression molecule, thereby abating the immune infiltration level of CD8+ cells in tumor tissue. As revealed by cellular assay, LINC00887 was upregulated in ccRCC cells, and knockdown of LINC00887 resulted in a decreased PD-L1 expression, increased CD8+ T cell toxicity, decreased apoptotic levels, and enhanced chemotaxis. Moreover, we found that LINC00887 exhibited inhibitory effect on immune infiltration of CD8+ cells in clinical tissues. Conclusions. The results of this study suggested that LINC00887 promoted ccRCC progression by inhibiting immune infiltration of CD8+ T cells, providing new insights into pathogenesis of ccRCC and suggesting LINC00887 being a promising immunotherapy target for ccRCC.
Approximating Dynamic Equilibrium Analysis in Multi-Region Network Based on Macroscopic Fundamental Diagram
Authors: Liu Bo , Cen Yanqing , Song Xianghui
Published: August 2022

Modeling and control of road traffic in large-scale urban networks present considerable challenges. The traffic equilibrium phenomena, with the question of route choice behavior in case of heterogeneous urban networks, has not been thoroughly investigated in parsimonious and classical models due to the limitation, like large network size, spatiotemporal propagation of congestion, and the interaction between driver decisions, etc. In this paper, we propose a bi-level approximating dynamic equilibrium model (BLADEM) for the approximating dynamic equilibrium analysis in multi-region network based on macroscopic fundamental diagram (MFD). The proposed model combines the region-based model and the internal-region model. With the information from region MFD, the region-based model is used to implement the time-dependent regional route choice estimation. Traffic equilibrium condition (dynamic user equilibrium, DUE) is considered in an internal-region model with time-aggregated regional OD demand from the region level. Furthermore, the complexity of the proposed model is derived. Then, the comparative analysis of the algorithm complexity between the proposed model and the DUE model is given. The proposed model is evaluated based on the high-resolution vehicle trajectory data (or connected vehicles trajectory data) from the DiDi platform collected in Chengdu, China with more than 3,000,000 GPS points during a typical workday. The evaluation results show that the proposed model can obtain the approximating traffic dynamics compared with the DUE algorithm. Pleasantly, the improved calculation efficiency is between 21% and 42%. The results indicate the promising potential of using the proposed model to analyze approximating dynamic equilibrium in the multi-region heterogeneous network.
Inflammation and Oxidative Stress Role of S100A12 as a Potential Diagnostic and Therapeutic Biomarker in Acute Myocardial Infarction
Authors: Xie Jian , Luo Changjun , Mo Binhai , Lin Yunhua , Liu Guoqing , Wang Xiantao , Li Lang
Published: August 2022

Acute myocardial infarction (AMI) is one of the most serious cardiovascular diseases with high morbidity and mortality. Numerous studies have indicated that S100A12 may has an essential role in the occurrence and development of AMI, and in-depth studies are currently lacking. The purpose of this study is to investigate the effect of S100A12 on inflammation and oxidative stress and to determine its clinical applicability in AMI. Here, AMI datasets used to explore the expression pattern of S100A12 in AMI were derived from the Gene Expression Omnibus (GEO) database. The pooled standard average deviation (SMD) was calculated to further determine S100A12 expression. The overlapping differentially expressed genes (DEGs) contained in all included datasets were recognized by the GEO2R tool. Then, functional enrichment analyses, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, were carried out to determine the molecular function of overlapping DEGs. Gene set enrichment analysis (GSEA) was conducted to determine unrevealed mechanisms of S100A12. Summary receiver operating characteristic (SROC) curve analysis and receiver operating characteristic (ROC) curve analysis were carried out to identify the diagnostic capabilities of S100A12. Moreover, we screened miRNAs targeting S100A12 using three online databases (miRWalk, TargetScan, and miRDB). In addition, by comprehensively using enzyme-linked immunosorbent assay (ELISA), real-time quantitative PCR (RT–qPCR), Western blotting (WB) methods, etc., we used the AC16 cells to validate the expression and underlying mechanism of S100A12. In our study, five datasets related to AMI, GSE24519, GSE60993, GSE66360, GSE97320, and GSE48060 were included; 412 overlapping DEGs were identified. Protein-protein interaction (PPI) network and functional analyses showed that S100A12 was a pivotal gene related to inflammation and oxidative stress. Then, S100A12 overexpression was identified based on the included datasets. The pooled standard average deviation (SMD) also showed that S100A12 was upregulated in AMI (

SMD
=
1.36

, 95% CI: 0.70-2.03,

p
=
0.024

). The SROC curve analysis result suggested that S100A12 had remarkable diagnostic ability in AMI (

AUC
=
0.90

, 95% CI: 0.87-0.92). And nine miRNAs targeting S100A12 were also identified. Additionally, the overexpression of S100A12 was further confirmed that it maybe promote inflammation and oxidative stress in AMI through comprehensive in vitro experiments. In summary, our study suggests that overexpressed S100A12 may be a latent diagnostic biomarker and therapeutic target of AMI that induces excessive inflammation and oxidative stress. Nine miRNAs targeting S100A12 may play a crucial role in AMI, but further studies are still needed. Our work provides a positive inspiration for the in-depth study of S100A12 in AMI.
An Outdoor Pedestrian Localization Scheme Fusing PDR and VPR
Authors: Liu Yu , Gong Qingyuan , Wang Shaochu , Zhang Liqiang , Xu Fang
Published: August 2022

With the popularity of phones, the demand for pedestrian location-based services has increased significantly. PDR (pedestrian dead reckoning algorithm) can provide pedestrians with real-time and continuous localization services in the outdoor environment. However, due to the accuracy of the built-in inertial sensor of the mobile phone collecting data, there is heading drift in PDR resulting in localization error. The localization method based on VPR (visual place recognition) can provide the accurate position of a single point. The VPR based on the lightweight model only generates a low amount of calculation. We propose an outdoor localization scheme fusing PDR and VPR to provide continuous localization service for outdoor pedestrian localization. The experiment results show that compared with the PDR algorithm, the ARMSE of the fusion localization scheme is reduced by 40% to 60%, and the final start-to-end error is reduced by 31.9 m. The fusion scheme can provide an accurate and continuous localization service.
Deducing Leading Factors of Spatial Distribution of Carbon Reserves in Nanjing Metropolitan Area Based on Random Forest Model
Authors: Xue Jiefu , Yan Jun
Published: August 2022

Improving carbon reserves is considered to be an important way to alleviate global warming. However, there is a lack of research work based on the perspective of metropolitan area, and there is also a lack of analysis on the leading influencing factors of spatial distribution of carbon storage in subregions of metropolitan area. In this study, Nanjing metropolitan area (NMA) is taken as the research area, and the InVEST model is used to calculate the spatial distribution of regional carbon reserves, and the evolution of carbon reserves distribution in recent 20 years is analyzed. Then, based on the random forest (RF) model, taking the whole study area and subareas as the research scope, a regression model of each selected impact factor and carbon reserves is established, and the leading factors of spatial distribution of carbon reserves in NMA are obtained. The results show that the overall carbon reserves level in the study area is in a downward trend. Through the application of the RF model, the leading factors of the spatial distribution of carbon reserves in NMA and its subareas are derived. The research proves that the application of the RF model in the analysis is helpful for city planners and governments to make plans and improve regional carbon storage more effectively.
Analysis of Application Data Mining to Capture Consumer Review Data on Booking Websites
Authors: Tsai Yao-Hsu , Lin Chien-Cheng , Lee Min-Hsien
Published: August 2022

The rapid development of the Internet has led to the prevalence of big data analysis. Data mining is crucial to extracting potentially valuable information from big data and has therefore received considerable attention from researchers. Python is a common programming language used in data mining. Because of its rich database and robust capacity for scientific calculations, Python is considered an irreplaceable tool for data mining. This study adopted Python to perform a data mining analysis on visitor comments on Booking.com. The study was divided into several stages, namely, data source selection, data acquisition, data saving, data preprocessing, indexing of comments on Booking.com through the Python-based Scrapy framework, and user operation simulation through Selenium to analyze the performance of the spider program. Data mining can be used to identify useful information, which can serve as references for consumers to make purchase decisions. Extraction of data from booking sites through spider programs enables site administrators to attract more visitors. Analysis of extracted data also facilitates the elimination of misjudged comments and helps hotels improve their service quality, hardware, and personnel training.
Androgen Plays a Potential Novel Hormonal Therapeutic Role in Th17 Cells Predominant Neutrophilic Severe Asthma by Attenuating BECs Regulated Th17 Cells Differentiation via MBD2 Expression
Authors: Wasti Binaya , Chen Zhifeng , Yuan Yu , He Yi , Duan Wentao , Jia Jingsi , Li Danhong , Xiao Bing , Li Jianmin , Liu Yi , Liu Shaokun , Zhang Dongshan , Zhang Xiufeng , Ma Libing , Xiang Xudong
Published: August 2022

T helper 17 (Th17) cells subtype of non-T2 asthma is less responsive (resistant) to inhaled corticosteroids (ICS), so also called severe asthma. Methyl-CpG-binding domain protein 2 (MBD2) regulates the differentiation of the Th17 cells, showing the possibility of a therapeutic target in severe asthma. Androgen tends to show beneficial therapeutic effects and is a “hot research topic,” but its role in the differentiation and expression of Th17 cells via MBD2 is still unknown. The aim of this study was to evaluate how sex hormone interacts with MBD2 and affects the differentiation and expression of Th17 cells in severe asthma. Here, first, we measured the concentration of androgen, estrogen, and androgen estrogen ratio from subjects and correlated it with severe asthma status. Then, we established an animal model and bronchial epithelial cells (BECs) model of severe asthma to evaluate the role of MBD2 in the differentiation and expression of Th17 cells (IL-17), the therapeutic potential of sex hormones in severe asthma, and the effect of sex hormones in BECs regulated Th17 cells differentiation via MBD2 at the cellular level. Increased MBD2 expression and Th17 cells differentiation were noted in the animal and the BECs severe asthma models. Th17 cell differentiation and expression were MBD2 dependent. Androgen attenuated the differentiation of BECs regulated Th17 cells via MBD2 showing BECs as a therapeutic target of androgen, and these findings postulate the novel role of androgen in Th17 cells predominant neutrophilic severe asthma therapy through targeting MBD2.
Construction of English Translation Teaching Model Based on Cloud Data Edge Detection Algorithm and Output-Oriented Method
Authors: Kong Fenghua
Published: August 2022

In the context of globalization, English, as a unique passport to the world, is an important way for countries to communicate in information, technology, and culture. The output-oriented approach is a new theory of foreign language learning and teaching constructed by Chinese scholars, which has attracted widespread attention from foreign language scholars and front-line teachers and is expected to address the issue of “separation of learning and use” of English in Chinese universities. In the context of globalization, English, as a unique passport to the world, is an essential way for countries to exchange ideas about information, technology, and culture. Therefore, there is no doubt that English education plays a crucial role in nurturing the younger generation, especially at the high school level. This paper introduces an edge detection algorithm and an output-oriented approach to the teaching model of English translation. Questionnaires were administered to students in the control and experimental classes. The percentages of students who strongly disagreed, disagreed, neutral, agreed, and strongly agreed with the translation task based on their interest were 1.9%, 13%, 25.1%, 36.8%, and 23.2%, respectively.
Simulation Analysis of External Damage and Repair of the Gas Transmission Pipeline
Authors: Wang Xinhui , Li Heng , Li Bing , Sheng Jie , Zhao Jinqiang , Ding Yanlong , Lu Duhui
Published: August 2022

External damage and repair of a gas transmission pipeline were often encountered in industrial production. The damage of the gas transmission pipeline and the evaluation after repair could be solved by the finite element simulation method. Damage assessment and postrepair assessment were carried out for an external damage example of a gas transmission pipeline in an oil field based on finite element software ABAQUS. Ideally, the finite element simulation results were modified by using the theoretical calculation results of the gas transmission pipeline. Then, the simulation analysis of the defective gas transmission pipeline was carried out, and the corresponding maintenance suggestions were put forward. Finally, the B-type sleeve repair and carbon fiber composite repair were simulated and analyzed, respectively, and the bearing and stress of two kinds of repair were compared and analyzed. It was concluded that the repair effect of the carbon fiber composite was better than that of B-type sleeve. The research results could provide a new evaluation mechanism for pipeline defects and pipeline defects after repair and laid a foundation for quantitative risk assessment and repair of gas transmission pipelines.
Analysis of the Application Effect of Multidisciplinary Team Cooperation Model in Chronic Heart Failure under WeChat Platform
Authors: Huang Jieyu , Su Yu , Mao Xiucai
Published: August 2022

Background and Purpose. Chronic heart failure (CHF) is a complex clinical syndrome with a gradually increasing incidence that has become a public health problem of global concern. With the improvement of treatment modalities, great progress has been made in its treatment, but patients’ recovery outcomes are poor. The aim of this study was to apply the multidisciplinary teamwork model under the WeChat platform to the treatment of patients with chronic heart failure. Methods. From April 2020 to May 2021, 56 patients with CHF who were discharged from the cardiology department of our hospital after treatment were randomly divided into two groups: experimental group (n = 28) and control group (n = 28). The control group was given conventional nursing measures and health education and discharge instructions, while the experimental group received collaborative multidisciplinary team nursing care based on the WeChat platform on the basis of the control group, all for 3 months. All enrolled patients underwent the Self-Care of Heart Failure Index Version 6.2 (SCHFI v6.2), the Minnesota Living with Heart Failure Questionnaire (MLHFQ), and the 6-minute walking test (6MWT test). The SCHFI v6.2 and MLHFQ scores, 6 MWT test results, and readmission rates within 3 months were observed and compared between the two groups. Results. There was no significant difference between the SCHFIv6.2 and MLHFQ scores of the two patients at admission and at discharge, and the scores of the experimental group were significantly higher than the scores of the control group at the end of 3 months after discharge. On the other hand, the SCHFIv6.2 and MLHFQ scores of the two groups were significantly higher at discharge compared to admission; the 6-minute walking distance of the experimental group was significantly higher than that of the control group at the end of 3 months. The readmission rate in the experimental group was significantly lower than that in the control group. Conclusion. The multidisciplinary teamwork model based on the WeChat platform can significantly improve the self-care ability and quality of life of CHF patients and reduce the readmission rate.
High‐Frequency Vibrating Stimuli Using the Low‐Cost Coin‐Type Motors for SSSEP‐Based BCI
Authors: Kim Keun-Tae , Choi Junhyuk , Jeong Ji Hyeok , Kim Hyungmin , Lee Song Joo
Published: August 2022

Steady‐state somatosensory‐evoked potential‐ (SSSEP‐) based brain‐computer interfaces (BCIs) have been applied for assisting people with physical disabilities since it does not require gaze fixation or long‐time training. Despite the advancement of various noninvasive electroencephalogram‐ (EEG‐) based BCI paradigms, researches on SSSEP with the various frequency range and related classification algorithms are relatively unsettled. In this study, we investigated the feasibility of classifying the SSSEP within high‐frequency vibration stimuli induced by a versatile coin‐type eccentric rotating mass (ERM) motor. Seven healthy subjects performed selective attention (SA) tasks with vibration stimuli attached to the left and right index fingers. Three EEG feature extraction methods, followed by a support vector machine (SVM) classifier, have been tested: common spatial pattern (CSP), filter‐bank CSP (FBCSP), and mutual information‐based best individual feature (MIBIF) selection after the FBCSP. Consequently, the FBCSP showed the highest performance at 71.5 ± 2.5% for classifying the left and right‐hand SA tasks than the other two methods (i.e., CSP and FBCSP‐MIBIF). Based on our findings and approach, the high‐frequency vibration stimuli using low‐cost coin motors with the FBCSP‐based feature selection can be potentially applied to developing practical SSSEP‐based BCI systems.
A Study of English Reading Modality Recognition and Practice Based on Intelligent Sensor Networks
Authors: Guo Xuan , Chen Fengping
Published: August 2022

Intelligent sensor networks are a current hot topic in the field of communication and are widely used in subject education, quality education, and home security monitoring. In today’s world of increasingly diverse services and growing information needs, wireless communication systems require more information to better understand and analyse the observed objects. In this context, this paper presents a study on the identification and practice of English reading modality based on intelligent sensor networks. The paper divides English reading modality into two parts: semantic modality and situational modality, and its system consists of three main modules: English reading article data collection, English article data semantic analysis, and English reading article situational picture feedback module. Finally, a practical study is carried out on the basis of this application, and it is concluded that the development and future prospects of this application are considerable.
Physical Activity Level and Social‐Ecological Influence Factors in Chinese Information Technology Professionals: A Cross‐Sectional Study
Authors: Wang Lejun , Yang Hua , Song Xiaoqian , Wang Ce , Qiao Minjie , Tao Haifeng , Niu Wenxin , Li Ruijie
Published: August 2022

Objective. Information technology (IT) population in China has expanded rapidly in recent decades, which has suffered severe health problems due to a lack of physical activity (PA). However, little is known about the influence and solutions of PA deficiency. The current research was designed to explore the associations between the amount of PA and potential influenced factors based on the social‐ecological model (SEM) and thus to provide rationales for PA promotion. Method. Six hundred and five IT professionals from five cities of China were surveyed in terms of PA in daily life as well as the potential PA influence factors based on SEM models that consisted of individual, interpersonal, environmental, and political levels in the current study. Hierarchical linear regression analyses were conducted to examine the association of the potential PA influence factors and PA amount. Result. About 54.7% of the sample did not fulfill the PA recommendation of 600 MET per week, and there are significant gender differences in PA participation. Factors related to the individual (self‐efficacy and value recognition), interpersonal (social support), environmental (workplace and community environment), and polity‐level factors (policy advocacy) were found to be significantly associated with Chinese IT professionals’ participation in PA. Conclusion. Numerous correlates from individual‐level to political‐level factors are all important predictors of PA. Exercise value recognition and self‐efficacy accounted for much of the association. Environmental variables may further influence exercise behavior. Therefore, conducting intervention efforts for individual, interpersonal, environmental, and political change of Chinese IT professionals is recommended.
A Two-Phase Method for Optimization of the SPARQL Query
Authors: Lin Xiaoqing , Jiang Dongyang
Published: August 2022

With a rapid growth in the available resource description framework (RDF) data from disparate domains, the SPARQL query processing with graph structures has become increasingly important. In this pursuit, we designed a two-phase SPARQL query optimization method to process the SPARQL query. The structural characteristics of RDF data graphs, predicate path sequence indices (PPS-indices), were used to efficiently prune the search space, which captured the inherent features of the RDF data graphs, while the database is updated. Our storage model was based on a relational database. Compared to a baseline solution, the proposed method effectively reduced the cardinalities of the intermediate results during the query processing, and at least an order of magnitude improvement is achieved in filtering performance, thereby improving the efficiency of the query execution.
Managing and Retrieving Bilingual Documents Using Artificial Intelligence-Based Ontological Framework
Authors: Alothman Abdulaziz Fahad , Wahab Sait Abdul Rahaman
Published: August 2022

In recent times, artificial intelligence (AI) methods have been applied in document and content management to make decisions and improve the organization’s functionalities. However, the lack of semantics and restricted metadata hinders the current document management technique from achieving a better outcome. E-Government activities demand a sophisticated approach to handle a large corpus of data and produce valuable insights. There is a lack of methods to manage and retrieve bilingual (Arabic and English) documents. Therefore, the study aims to develop an ontology-based AI framework for managing documents. A testbed is employed to simulate the existing and proposed framework for the performance evaluation. Initially, a data extraction methodology is utilized to extract Arabic and English content from 77 documents. Researchers developed a bilingual dictionary to teach the proposed information retrieval technique. A classifier based on the Naïve Bayes approach is designed to identify the documents’ relations. Finally, a ranking approach based on link analysis is used for ranking the documents according to the users’ queries. The benchmark evaluation metrics are applied to measure the performance of the proposed ontological framework. The findings suggest that the proposed framework offers supreme results and outperforms the existing framework.
Image Video Teaching Method in College Physical Education
Authors: Xu Haibo , Wan Lun
Published: August 2022

At present, traditional methods of physical education in colleges and universities are no longer able to meet the requirements of modern physical education and need to be improved. The application of the visual teaching method to the teaching of physical education in college in the classroom can best see the modern theory of physical education, which is necessary for the theory of physical education in college. On this basis, it is recommended to use the teaching method with image in the teaching of physical education in college. This document is in the form of a search query in conjunction with other search methods to perform a search. This article takes Tai Chi in the university physical education curriculum as an example. It selects two classes with no difference in number of genders and conducts traditional teaching and VR image video teaching, respectively. And from the learning situation, interest and satisfaction of the two classes analyze the degree. In this study, we found that before the experiment, the number of students with 85 scores or above in the control class was 8, accounting for 20%, 14 students with 70-85 scores, accounting for 35%, and 18 students with 60-70 scores, accounting for 45%. As could be observed, the majority of students in the control class scored between 60 and 70 points overall, with a merit rate of only 55%. In the experimental class, there are 15 students with 85 points or above, accounting for 37.5%, 18 students with 70-85 points, accounting for 45%, and 7 students with 60-70 points, accounting for 17.5%. In the experimental class, the overall score of students is between 70 and 85 points, accounting for 82.5%. The image and video teaching method can be seen to be useful in improving students’ athletic achievement. In addition, the image and image instruction approach can better enhance students’ interest in learning, and most students are satisfied with the image and video instruction approach.
[Retracted] Exploration of the Application Effect of the Darongtong Course Model Based on Deep Learning Enhancement in Nursing
Authors: Han Qi , Ma Chunhua , Wang Ronghua , Xu Chen
Published: August 2022

In order to explore the application effect of the Darongtong course model based on deep learning enhancement in nursing, a total of 500 students in the school are investigated. The students in the contrast set are given the traditional teaching mode, and the students in the research set are provided with a teaching method based on deep learning‐enhanced Darongtong mode. The evaluation results, teaching quality, students' cognition of teaching effect and their satisfaction with teaching mode before and after teaching were compared between the two groups. From the experimental results, it can be seen that the application of Datong general teaching based on deep learning in school nursing students can not only deepen students' understanding of theoretical knowledge, improve clinical skills, cultivate students' nurse patient communication skills and clinical thinking, but also improve students' subjective initiative. It is clearly evident that the Darongtong course model based on deep learning enhancement in nursing can stimulate students' interest in learning, and improve their self‐study ability and problem‐solving ability effectively.
A Study on the Construction of Translation Curriculum System for English Majors from the Perspective of Human-Computer Interaction
Authors: Li Qi
Published: August 2022

This paper proposes a collaborative human-computer interaction recommendation model for English major translation courses to address the problems of poor course recommendation and lack of robustness to noisy data in traditional recommendation models for English major translation courses. First, a new data enhancement method is proposed for the bipartite graph structure. Then, the enhanced data is fed into a graph convolutional neural network for node feature extraction to obtain node representations of users and items. A recommendation supervision task and an auxiliary task for contrast learning are constructed for joint optimization. The human-computer interaction model of the knowledge graph is designed, and the dialogue entities are embedded in the knowledge graph ripple network to obtain potentially interesting content for students. Finally, the student interaction content and node representations are combined to obtain the optimal translation course recommendation. The experimental results indicate that the proposed model in this work is capable of producing higher-quality English major course recommendations and beats other current models. This model is suitable for English primary translation course content recommendation and helps to improve students’ translation ability.
Improving and Evaluating Business Management in the Digital Economy Based on Data Analysis
Authors: Sun Lu , Wang Yanwen
Published: August 2022

In the new economic situation, enterprises formally enter the transition period of “think power differentiation and gap,” big data, artificial intelligence, digital empowerment, and other new digital economy has a positive effect on improving technical means and reducing operating costs, but the digital economy also forces enterprises to improve their strategies at a multiplier speed. The strategic decision of enterprises is to de-enterprise, from pursuing a single business operation benefit to winning competitive advantage by creating a platform ecosystem; the enterprise organization management mode is shifting from vertical management to horizontal collaborative management, and the organization structure is developing dynamically flat. This requires enterprises to pay attention to the role of data analysis in daily operations and management, promote the establishment of a data-driven corporate culture, make full use of digital technology, modularize production links or departments with similar functions or services, and promote the transformation of organizational structure. By unifying the output of products or services to the outside world; at the same time, improve the corporate performance model, shift from result-oriented to process-oriented assessment, and implement a personalized, authority, and accountability system. Shift from result-oriented to process-oriented assessment and implement a personalized incentive model that matches authority, responsibility, and benefits.
Discuss the Application of Data Services in Data Health Management of High-Risk Pregnant and Lying-In Women in Smart Medical Care
Authors: Shen Leifen , Shi Weiqin , Cai Liwen , An Jing , Ling Qian
Published: August 2022

Objective. In order to improve the refined management of hospitals, promote the scientific development of smart hospitals in medical institutions, and solve the problem of data filling and reporting that is increasing year by year in the country, province, and city. Methods. A total of 84 high-risk pregnant women admitted to our hospital from January 2020 to October 2021 were selected and screened for high-risk pregnant women. Risk pregnant women were divided into a routine intervention group and a DS medical group, with 42 cases in each group. High-risk pregnant women in the routine intervention group received routine intervention, and the DS medical group applied data to serve smart medical services on the basis of routine intervention. The scores of self-care, anxiety, and depression were compared between the two groups, the coping styles were analyzed, the satisfaction rate and incidence of adverse conditions of the high-risk puerperae were recorded, and the delivery methods of the two groups were compared. Results. After the intervention, the activities of daily living, follow-up, fetal monitoring, and self-protection behaviors in the DS medical group were higher than those in the routine intervention group, and the difference was statistically significant (

P
<
0.05

). The scores of anxiety and depression in the group were lower, with statistical significance (

P
<
0.05

); after the intervention, the scores of negative coping styles in the DS medical group were lower than those in the conventional intervention group, while the scores for positive coping styles were higher than those in the conventional intervention group; the DS medical group had higher risk. The satisfaction of pregnant women was significantly higher than that of the routine intervention group, and the difference was statistically significant (

P
<
0.05

); the overall incidence of adverse maternal outcomes among high-risk pregnant women in the DS medical group was lower than that of the routine intervention group, and the difference was not statistically significant (

P
>
0.05

). Compared with the routine group, the DS medical group had a higher number of vaginal deliveries and a lower number of cesarean deliveries, and the difference was statistically significant (

P
<
0.05

). Conclusion. The application of data services in a smart medical high-risk maternity-related data management platform enables the promotion of high-risk pregnant women’s self-care behaviors and improves negative emotions, enables them to cooperate in delivery with positive behaviors, and reduces the number of cases of cesarean delivery.
Driven Force Induced Bifurcation Delay on the Chaotic Financial System
Authors: Ramakrishnan Balamurali , Abdalla Mohamed , Boulaaras Salah , Rajagopal Karthikeyan
Published: August 2022

To understand the variations in the financial characteristics, we examine the dynamical behaviors by considering the chaotic financial model with external force. First, the dynamical characteristics are analyzed by introducing the external driven force in the price index with commodity demand. We discover that the presence of an external force causes the alternate occurrence of oscillatory and steady states as a function of time. Interestingly, we find the existence of bifurcation delay (BD) during the transition from oscillatory (OS) to steady state (SS) or vice versa. Bifurcation delay is a phenomenon in which the bifurcation does not occur at the actual bifurcation point but rather at a later time, which is referred to as bifurcation delay. To confirm the delay in bifurcation, we estimate the actual bifurcation point and compare it to the observed bifurcation transition. Furthermore, to understand the variations in the bifurcation delay, we estimate the delay time between each consecutive cycle and find random fluctuations in the BD. Following that, the BD is virtualized via a transformed phase portrait. In addition, we show decreasing the value of average BD while increasing the frequency of external forcing. Second, the presence of BD is explored by incorporating external forces into the investment demand with unit investment cost. We discover the existence of a similar phenomenon with a constant bifurcation delay.
Location Mechanism and Perception Scheme of Remote Transmission Equipment Layout in Smart Water
Authors: Xing Shikai , Xie Xiang
Published: August 2022

Whether the traditional water equipment is equipped with remote transmission unit depends on whether the power consumption of the installed equipment can meet the expected life cycle. In the paper, by using intelligent strategies to save the power consumption of sending data, more sites can be selected to install remote transmitting units. In the process of transmitting data packet sequence, the remote transmission device needs to perceive the environment, interact with the environment and make decisions, and adjust the strategy according to the effect of the decision actions. Therefore, in this paper, the transmission process is modeled as Markov sequence decision model. And the real time signal interference noise ratio of the channel and the transmission delay of the data packet are defined as the state space. The decision action space consists of immediate transmission and delayed one, and the minimum total power consumption is taken as the objective function. The model is solved by Proximal Policy Optimization (PPO) algorithm, and the optimal decision sequence of site selection threshold is obtained.
Fracture Properties of Recycled Concrete Reinforced with Nanosilica and Steel Fibers
Authors: Wang Yonggui , Zhang Xuetong
Published: August 2022

To promote the engineering application of recycled concrete, recycled concrete is modified by the addition of nanosilica (NS) and steel fibers (SF) to form NS and SF reinforced recycled concrete (NSFRC). The effects of the replacement rate and the SF and NS content on the fracture properties of recycled concrete (RC) are analyzed via a three-point bending test. The research shows that the fracture surface of the specimen is relatively flat when no SF are added. With an increase in the SF or NS content, the initial cracking load, initiation fracture toughness, instability load, and fracture energy of the NSFRC increase. The fracture properties of RC composites reinforced with SF and NS are better than those of RC reinforced with a single material. When 2% NS and 1.5% SF are added, the fracture properties reach a maximum value. The damage model established in this study can better reflect the stress-strain relationship of NSFRC during fracture.
Implementation of Personalized Information Recommendation Platform System Based on Deep Learning Tourism
Authors: Wang Xuejuan
Published: August 2022

In order to provide tourists with better tourism services, a system method of personal information recommendation platform based on deep learning tourism is proposed. The system includes noise reduction autoencoder, feature extraction module, data preprocessing module, recommendation calculation module, expert evaluation module, recommendation result output module, customer feedback module, and storage module. The personal information recommendation platform system based on deep learning tourism of the present invention enables tourists to obtain tourism information conveniently and quickly through scientific information organization and presentation form and helps tourists to better arrange tourism plans and form tourism decisions. By effectively aggregating multiple neighborhoods of nodes, embedding high-order collaboration information into the node embedding vector, obtaining the potential preferences of users, solving the problems of user data sparse and cold start, and finally through experimental analysis, a research method is proposed. It is used to build the model of tourist attraction recommendation system. Experimental results show that the proposed method for cold-start user recommendation has the best performance in terms of accuracy, recall, and normalized loss cumulative gain, and it is 17.9% higher than BPR in recall rate Recall@5 and 11.8% higher in accuracy rate. It is proved that the system has a significant impact on the diversity and novelty of tourist attraction recommendation.
Analysis of the Mechanism of GuizhiFuling Wan in Treating Adenomyosis Based on Network Pharmacology Combined with Molecular Docking and Experimental Verification
Authors: Shi Yaxin , Zhang Chengyuan , Wang Xin , Wang Zilu , Zhang Yiran , Liu Zhiyong , Wang Xin , Shi Wei
Published: August 2022

Background. The effect of GuizhiFuling Wan (GFW) on adenomyosis (AM) is definite. This study aimed to explore the mechanism and key therapeutic targets of GFW in treating AM through network pharmacology combined with molecular docking and experimental verification. Materials and Methods. In network pharmacology, firstly, the active components of GFW, its drug, and disease targets were screened through several related public databases, and GFW-AM common targets were obtained after the intersection. Then, the biological function (Gene Ontology, GO) and pathway (Kyoto Encyclopedia of Genes and Genomes, KEGG) of GFW in treating AM were enriched and analyzed. Finally, the interaction and binding force between key components and key targets of GFW were verified by molecular docking. In the animal part, the effect of GFW on the expression of matrix metallopeptidase 2 (MMP-2), matrix metallopeptidase 9 (MMP-9), and vascular endothelial growth factor (VEGF) in mice with AM was observed by HE staining, ELISA, and immunohistochemistry. Results. In this study, 89 active components of GFW, 102 related targets, and 291 targets of AM were collected. After the intersection, 26 common targets were finally obtained. The key active compounds were baicalein, sitosterol, and β-sitosterol, and the key targets were MMP-2, MMP-9, and VEGF. GO and KEGG enrichment analyses showed that biological processes such as the positive regulation of vascular endothelial migration and signaling pathways such as TNF and HIF-1 were involved in regulating angiogenesis, invasion, and metastasis in AM. The molecular docking results showed that baicalein, β-sitosterol, and stigmasterol had better binding potential with MMP-2, MMP-9, and VEGF. The results of in vivo analysis showed that GFW could decrease the serum content and protein expression of MMP-2, MMP-9, and VEGF in mice with AM. Conclusions. GFW could reduce the expression of MMP-2, MMP-9, and VEGF, which might be an essential mechanism for GFW to inhibit the invasion and metastasis of ectopic tissues of AM.