bluuebunny/crossref_metadata_2025 huggingface dataset:
Construction and Analysis of Urban Traffic Noise Prediction Model Based on PSO Algorithm and BRF Network Structure
Authors: Qiu Yuehui
Published: August 2022

Due to the current nonlinear traffic noise and changing natural environment, accurate prediction of traffic noise has become a major challenge. In order to improve the accuracy of traffic noise prediction, this paper constructs a short-term traffic noise prediction model based on PSO algorithm and BRF network structure. The model can predict the noise state of future traffic according to historical traffic information. Particle swarm optimization (PSO) network is used for short-term traffic prediction to deal with the problems of low accuracy and long-time consumption. A BRF (one-dimensional convolution)-PSO short-term traffic noise prediction model is proposed. BRF method is used to extract the spatial features of traffic noise and compared with PCA (principal component analysis)-PSO model and PSO model. The results show that the prediction accuracy of BRF-PSO model is 2% higher than that of PCA-PSO model and 6% higher than that of PSO model, which proves the effectiveness of BRF-PSO model. This study provides a technical reference for the short-term traffic noise prediction of intelligent city and the effective improvement of urban traffic environment.
Nonlinear Random Matrix Model and Research for Quantitative Representation of Volleyball Attacker’s Action Links
Authors: Li Fangshu , Jia Nian , Wang Haomiao , Zheng Hua
Published: August 2022

With the promotion of volleyball, the technical and tactical level of volleyball is also constantly improving, and the competition in volleyball competition is also extremely fierce. Especially in important games, it can be seen that most of the outstanding volleyball players have the characteristics of both offense and defence. The outside hitter is the main attack point and end point of the team’s offense and defence. The outside hitter’s offensive and defensive ability plays a crucial role in the final outcome of the game. It can be said that an excellent outside hitter is one of the main indicators to measure whether a team can become a world volleyball team. This paper introduces and analyses the optimal solution of kernel norm minimization and its application in the RPCA model. Through analysis, we know that when the alternating direction method solves the RPCA problem, the most expensive calculation is the optimization of the nuclear norm. Facing the singular value decomposition calculation of large-scale matrices, in order to solve the shortcomings of time-consuming and memory-consuming to solve large-scale matrix calculations, this paper proposes new stochastic singular value decomposition algorithms. The outside hitters use the rubbing, slapping, and hanging techniques to adjust their offense, but they rarely use them in the setter’s active organization. Through the research on adjusting the offense, it is found that the outside hitter’s advantage in adjusting the offense not only compensates for the impact of the fluctuating first pass and inadequate defence but also resolves the opponent’s sharp serve and spike, which increases the morale of the team. It is the most important condition for the women’s volleyball team to win the Olympic Games. Overall, the scoring rate of the outside hitter in each offensive position is analysed, which provides important data parameters for the women’s volleyball team to distribute the ball.
[Retracted] Menopausal Symptoms and Sleep Quality in Women Aged 40–65 Years
Authors: Song Zicheng , Jiang Rongzhen , Li Changbin , Jin Feng , Tao Minfang
Published: August 2022

The study examined the relationship between menopausal symptoms and sleep disturbances and the related influencing factors. Methods. We recruited women aged 40‐65 years who attended the menopause clinic at Shanghai Jiao Tong University’s Sixth People’s Hospital from February 2011 to November 2019. The Menopause Rating Scale (MRS) was used to collect women’s menopausal symptoms, and the Pittsburgh Sleep Quality Index (PSQI) was used to evaluate the subjects’ sleep condition. We used logistic regression models to identify the relationship between menopausal symptoms and sleep quality. Results. A total of 1341 participants were recruited in this study. The most frequent three symptoms assessed by MRS were fatigue (72.9%), sleep disturbance (67%), and hot flashes with night sweats (65%). Participants’ age was significantly associated with the severity of menopausal syndrome (P < 0.01). According to the PSQI sleep evaluation, 66.9 percent of participants had sleep disturbances (PSQI > 5). Logistic regression analysis revealed that women with mild, moderate, or severe menopausal syndrome had a 3‐, 7‐, and 17‐fold increased chance of having sleep disturbances compared to women without menopausal syndrome. Conclusion. Women aged 40–65 years were found to have a significantly higher risk of menopausal syndrome and sleep disturbances.
Standardization of Environmental Responsibility Behavior of Production and Manufacturing Enterprises in the Context of Green Economy
Authors: Chen Lei , Lin Wen
Published: August 2022

The motivation and decision‐making of an enterprise are significantly affected by the green economy and the environmental and economic policies (EEPs). It is practical to deeply analyze the standardization mechanism and actual effect of environmental responsibility behavior (ERB) in enterprises. This study explores the ERB standardization in the context of green economy. First, a conceptual model was established for the action mechanism of the factors driving ERB standardization in enterprises, and an empirical model was developed around the concept of policy‐behavior‐performance, before investigating the influence of the ERB of production and manufacturing enterprises (PMEs) over their production benefit (PB). From the angle of motivation‐supervision theory, the authors examined how the supervision and motivation of EEPs affect the large panel PMEs’ sense of social responsibility (SSR) and ERB. Finally, the variables like marketization index (MI) and return on assets (ROA) were regressed, and the ERB standard level of PMEs in different industries was analyzed statistically. The results demonstrate the robustness of our empirical model. Finally, opinions and suggestions were provided to guide or constrain environmental and economic policies.
Personalized Recommendation Algorithm for Movie Data Combining Rating Matrix and User Subjective Preference
Authors: Liu Chang
Published: August 2022

The film industry has also caught the fast train of Internet development. Various movie resources have come into view. Users need to spend a lot of time searching for movies they are interested in. This method wastes time and is very bad. The article proposes an NMF personalized movie recommendation algorithm, which can recommend movies to users based on their historical behavior and preference. The research results of the article show the following: (1) the experiment counts movie reviews of different users in the same time span. The results show that 48.42% of users have only commented on a movie once, 79.76% of users have posted less than or equal to 5 comments, and 89.92% of user reviews have posted less than or equal to 10 times. (2) In the comparative experiments of the NMF algorithm in different dimensions, the effect of the NMF-E algorithm is much better than that of the NMF-A algorithm. The accuracy, recall, and F1 value of the NME-E algorithm are all 3 types. The experimental results show that the NME-E algorithm is the best among all algorithms. (3) In the experiment to test the effectiveness of the NMF personalized recommendation algorithm, comparing the experimental results, the MAE value of the improved NMF personalized recommendation algorithm is lower than that of the unimproved algorithm. When the number of neighbors is 10, the highest value of the improved MAE of the previous algorithm is 0.837. After the improved algorithm, the MAE value is the highest (0.83), and the MAE value has dropped by 0.007, indicating that the error is smaller after the improved algorithm, and the result of recommending movies is more accurate. The recall value of the four algorithms will increase as the number of neighbors increases. Among them, the recall value of the NMF algorithm proposed in the article is the highest among several algorithms. The highest value can reach 0.200, which is higher than the highest value of other algorithms. It shows that the recommendation effect of NMF algorithm is the best. (4) According to the results of the questionnaire, after using the NMF personalized recommendation algorithm, users’ satisfaction increased from 20% to 50%, an increase of 30%, and their dissatisfaction decreased from 15% to 8%, a decrease of 7%. Relative satisfaction increased from 52% to 55%, an increase of 3%, satisfaction increased from 35% to 60%, an increase of 25%, and dissatisfaction decreased from 40% to 20%, a decrease of 20%, indicating that the algorithm can meet the requirements of most people.
Wavelet-Based Multiscale Decomposition Algorithm for Trajectory Capture of Tennis Serves
Authors: Wang Shiguang , Cui Yaozhen
Published: August 2022

Tennis is a sport that is enjoyed by young and old alike. Participants can decide the style of play and control the speed and spin of the ball according to their ability, so the physical demands on participants are not high and the amount of exercise can be effectively controlled. With the increasing demand for quality of life, the population of tennis is growing rapidly. People have taken up tennis as a form of recreation. For professional players, the serve, as the foundation of the introduction to tennis, has been a challenge. A good serve can effectively control the situation on the court and even turn defeat into victory. Therefore, this paper focuses on capturing tennis trajectories using multidimensional wavelet segmentation in an attempt to provide an effective reference for tennis. The results show that our method has a good trajectory capturing effect and can correct and guide the tennis serving action well.
Enterprise Management Resource Protection System Based on Digital Information Technology
Authors: Zhou Wenya
Published: August 2022

Enterprise management resource protection is a system management activity in which an enterprise conducts comprehensive planning, allocation, utilization, and development of its resources, and it is the process of organizing, planning, coordinating, supervising, and controlling the allocation, utilization, and development of enterprise resources. The purpose of this paper is to analyze the system that digital information technology applies to enterprise management by constructing models and combinations based on digital information technology so that the built models and combinations have a more effective analysis and research. This paper first gives a general introduction to digital information technology, then analyzes the theory of enterprise management resource protection, then establishes a model for information technology to act on enterprise management, and finally analyzes the combination of information technology and enterprise management and compares the two through enterprise case analysis. The experimental results show that in the case of enterprise management in various regions, the efficiency of enterprise management based on digital information technology is obviously higher, and the enterprise resources can be better protected. From 2018 to 2021, the development of enterprise management resource protection in various regions is basically showing an upward trend; it increased from 3.443, 5.414, 6.473, 4.382, 2.997, 3.751, 0.506, and 2.974 in 2018 to 6.187, 7.658, 8.601, 7.518, 5.932, 6.516, 3.877, and 6.243 in 2021. This is a good demonstration of the effectiveness of the protection of enterprise management resources based on digital information technology.
A Convex Approach for Trajectory Optimization of Super-Synchronous-Transfer-Orbit Large Launch Systems with Time-Position Constraints
Authors: Li Yuan , Sun Ruisheng , Chen Wei , Yang Nan
Published: August 2022

This paper presents a trajectory optimization algorithm for super-synchronous-transfer-orbit (SSTO) large launch systems by convex optimization. The payload of SSTO launch systems is typically a geostationary equatorial orbit (GEO) satellite, and the time and position of orbital injection are constrained, which is quite different from the case of general satellites. In this paper, the optimal control problem of SSTO large launch systems is formulated considering the terminal constraints including orbital elements and the time-position equation. To improve the computational performance of the algorithm, the terminal orbital element constraints are expressed in the perifocal coordinate system with second-order equations. And then, several convexification techniques and their modified strategies are applied to transform the original trajectory optimization problem into a series of convex optimization problems, which can be solved iteratively with high accuracy and computational efficiency. Considering the time-position constraint of the payload, the flight time updater design method is proposed to correct the error of time during the flight, which lays solid foundation for the subsequent flight phase, guaranteeing that the GEO satellite settles into the required position. Finally, simulation results indicate the high efficiency and accuracy and strong robustness of the proposed algorithm in different special situations including engine failure and time delay. The algorithm proposed in this paper has great development potential and application prospect in onboard trajectory optimization of SSTO launch missions and similar situations.
Sentiment Analysis of Texts on Public Health Emergencies Based on Social Media Data Mining
Authors: Hu Nan
Published: August 2022

Background. Since the COVID-19 pandemic, social media has become an important arena for the public to transmit and exchange messages, feelings, opinions, and information about the epidemic. In the era of social media, many UGC contents from self-media and various information about the epidemic on social media have strong emotional colors. These contents are not only rich in resources for text sentiment analysis but also reveal the laws and characteristics of the evolution of users’ emotional tendencies in public health emergencies. It even ties together the interaction between media content and society. Objective. As the Sina Weibo platform’s characteristics of communication are real-time, open, and “many-to-many,” the objective of this study is to collect Weibo-blog contents tagged with the outbreak of COVID-19 in a certain metropolis in China and analyze the emotional evolution situation of Weibo-blogs of the unexpected public health emergency involved. This will provide a dynamic understanding of the mechanisms underlying the evolution of emotional conditions in the context of public health emergencies. Methods. This paper uses a Python crawler, the SnowNLP sentiment analysis model, and correlation analysis to calculate the emotional tendencies of the event “Covid-19 outbreak in a Chinese metropolis” on the Sina Weibo platform. The study was carried out in terms of the evolutionary stages of the event and the factors which induce it. Results. This paper revealed characteristics of time-varying laws and dynamic propagation of users’ emotional evolution in public health emergencies. (1) This study refers to the life cycle model of COVID-19, combined with the statistics of the time series of the quantities of Weibo-blog posting, and divides the law of the quantities of Weibo-blog posting changing with the event into three stages: outbreak period, stalemate period, and resolution period. (2) Users’ emotional tendencies are changeable and unstable which are easily induced by various factors. (3) There is a significant positive correlation between the reported confirmed cases and the quantities of Weibo-blog posts. (4) Individual emotional tendencies will have a positive changing trend with the public’s average emotional tendencies after the event occurs. (5) There is no correlation between reposts, comments, and Weibo-blog emotional tendencies. Conclusion. The research found that, given staged evolution and repeated fluctuations of emotional tendencies, relevant departments should effectively use this law and set up different response plans according to different stages. In addition, what is highly coupled with users’ emotional tendencies is not only the information about the virus but more large-scale infected by different intensities of emotions.
Translytics: A Novel Approach for Runtime Selection of Database Layout Based on User’s Context
Authors: Tanvir Muhammad Makhshif , Shahzad Muhammad Khuram , Anwar Muhammad , Nam Su Man
Published: August 2022

Currently, organizations have to maintain separate systems for transactions and analytics inside the company. Notably, different vendors provide the capabilities for either of these tasks that require specialized hardware or software. Data engineers are required to retrieve data from one source and transform it into another format to obtain the maximum benefit in a minimum time. Organizations strive for a competitive advantage that is achieved by fetching data from their customers and getting insights earliest for timely decision making. Present practices do not permit the view of the latest data for analytics since the first data have to be fetched from the source, transformed, and loaded to other systems to be utilized for analysis by relevant teams. This paper introduces a single system for both transactions and analytics. Our proposed solution would permit companies to seamlessly adapt our solution without the need to shift all of their data to newer systems and allow all the teams. It would grant all the teams to have a view of the latest available data without extra expertise and budget.
Automated Recognition of Cancer Tissues through Deep Learning Framework from the Photoacoustic Specimen
Authors: Sobhanan Warrier Gayathry , Amirthalakshmi T. M. , Nimala K. , Thaj Mary Delsy T. , Stella Rose Malar P. , Ramkumar G. , Raju Raja
Published: August 2022

The fast advancement of biomedical research technology has expanded and enhanced the spectrum of diagnostic instruments. Various research groups have found optical imaging, ultrasonic imaging, and magnetic resonance imaging to create multifunctional devices that are critical for biomedical activities. Multispectral photoacoustic imaging that integrates the ideas of optical and ultrasonic technologies is one of the most essential instruments. At the same time, early cancer identification is becoming increasingly important in order to minimize fatality. Deep learning (DL) techniques have recently advanced to the point where they can be used to diagnose and classify cancer using biological images. This paper describes a hybrid optimization method that combines in‐depth transfer learning‐based cancer detection with multispectral photoacoustic imaging. The goal of the PS‐ACO‐RNN approach is to use ultrasound images to detect and classify the presence of cancer. Bilateral filtration (BF) is often used as a noise removal approach in image processing. In addition, lightweight LEDNet models are used to separate the biological images. A feature extractor with particle swarm with ant colony optimization (PS‐ACO) paradigm can also be used. Finally, biological images assign appropriate class labels using a recurrent neural network (RNN) model. The effectiveness of the PS‐ACO‐RNN technique is verified using a benchmark database, and test results show that the PS‐ACO‐RNN approach works better than current approaches.
Biomedical Disabled Rehabilitation Analysis considering Fuzzy Parameter Adaptive PID Algorithm
Authors: Zhang Baihui , Zhang Boyang , Zhao Hong
Published: August 2022

Fuzzy parameter adaptation refers to an idea of adjusting parameters based on parameter deviation, deviation change rate and the relationship between the parameters. The PID control algorithm incorporates three algorithms: ratio, sigma, and lead. It is one of the most commonly used algorithms and has a wide range of applications. Embedded system is an application-centric computer system based on modern computer technology that can be flexibly dedicated according to user needs. Although it is convenient to use, it cannot perform large-scale operations and storage. This article aims to explore the role of fuzzy parameter adaptive PID algorithm in the rehabilitation of biomedical persons with disabilities. It is hoped that modern technology will be used to find a new direction for the rehabilitation of the disabled, so that the disabled can better survive in the society. This paper studies the existing classic real-time scheduling algorithms and traditional adaptive scheduling algorithms and analyzes their shortcomings when applied to embedded systems; when sorting out related concepts, clarified related concepts such as disabled persons, to provide tertiary level study for people at risk and higher education policies for persons with disabilities; it combines classic and fuzzy control to form fuzzy self-adaptation, realizes fuzzy self-adaptive control of the temperature value of high temperature sterilization, and improves the automation level of the system. The experimental results of this article show that 68 persons with disabilities need medical diagnosis, accounting for 52%; 15 persons with disabilities need physical correction, accounting for 12%; 29 persons with disabilities require acupuncture treatment, accounting for 22%; 18 persons with disabilities were in mental treatment, accounting for 14%; 57 people lost the ability to learn due to memory decline, accounting for 44%; 46 people were unable to learn due to physical disabilities, accounting for 35%; but there were still 17 people who were able to study normally, accounting for 13% of the total number of people surveyed; 10 people were unable to study due to other reasons, accounting for 8%.
Internet of Things-Enabled Optimal Data Aggregation Approach for the Intelligent Surveillance Systems
Authors: Rahmani Mohammad Khalid Imam , Khan Fazlullah , Muzaffar Abdul Wahab , Jan Mian Ahmad
Published: August 2022

The Internet of Things (IoT)-based intelligent surveillance systems in smart cities are a challenging issue as various devices capture the data. These devices, deployed close to the underlined phenomenon, such as cameras, are duplicated or redundant as accuracy is the main requirement of these systems. For this purpose, sensor nodes are deployed to provide 24/7 monitoring of a smart city, which minimizes the security risks and enables quick response in case of any disaster. However, due to a large number of devices, huge data are generated; thus, controlling traffic congestion in case of undesirable circumstances, for example, in case of accident or intention blockage of the road, is desperately needed. Numerous data aggregation mechanisms were reported in the literature to address this issue with smart city surveillance systems. However, these approaches were designed for either specific application environments or complex environments, making the implementation process hard. In this article, we have developed an Internet of Things-enabled optimal data aggregation approach, specifically designed for the intelligent surveillance systems in smart cities, to convert raw data values into the refined ones with minimum possible data loss ratio. Moreover, the proposed scheme bounds every server to perform or carry out the data refinement process to maintain the expected ratio of accuracy and precision. In this approach, ordinary devices are forced to capture and forward data in raw form, preferably without any or minimum possible processing. This reduces the load on ordinary devices in intelligent surveillance systems. Additionally, we have developed a novel approach to eliminate or reduce (if elimination is not possible) noisy data or outliers. It is implemented along with existing state-of-the-art techniques to verify the exceptional performance of the proposed data aggregation approach. These algorithms were compared using various performance evaluation metrics such as refinement ratio, data loss ratio, energy efficiency, and lifetime. The simulation results have verified that the proposed scheme’s performance is better than the existing approaches.
Study on Prediction Model of Mechanical Parameters of Rock Frozen-Thawed Damage based on NMR Technology
Authors: Tai Yanzhi
Published: August 2022

In order to establish prediction models for the mechanical parameters of rock freeze-thaw damage based on nuclear magnetic resonance (NMR) technology, with reference to the laboratory test of rock mechanical parameters after freeze-thaw, combined with low-field NMR and multivariate analysis methods, PLSR and PCR prediction models for the peak stress, peak strain, and elastic modulus of frozen-thawed rocks were established. The results show that the correlation coefficient of calibration set (

R

2
cal

) and validation set (

R

2
val

) of the PLSR and PCR prediction models are both greater than 0.9, and the residual prediction deviation (RPD) of each model is greater than 3, indicating that the established prediction models have good stability, small relative error, and high prediction accuracy. The application evaluation results show that the peak stress and peak strain of frozen-thawed rocks can be accurately predicted using these models. In this paper, only the NMR tests are performed on the frozen-thawed rocks, and no rock mechanics experiments are performed. The research results provide a new method for the research of rock freeze-thaw damage.
Interval Short-Term Traffic Flow Prediction Method Based on CEEMDAN-SE Nosie Reduction and LSTM Optimized by GWO
Authors: Zhao Wenzheng , Yang Yuer , Lu Zihao
Published: August 2022

With rapid economic growth and urbanization, the accelerated increase in car ownership has brought massive pressure on urban traffic, and accurate traffic flow prediction information can provide an important basis for urban traffic dynamic planning. The existing methods have problems such as low efficiency, large error, and inability to adapt to short-term traffic changes. To solve the above problems, the CEEMDAN-SE-GWO-LSTM method was proposed in this paper. First, the traffic flow data is processed for outliers and missing values. The Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method is used to decompose the traffic flow data, and sample entropy (SE) is used to reconstruct the subsequence, which is used to improve the quality of the input data. Then, the Grey Wolf Optimizer (GWO) is used to optimize the parameters of the long-short-term memory (LSTM) in order to improve the prediction accuracy and prevent the model from falling into a local optimum. Three models are used to compare with the ensemble model proposed in this paper, including back propagation neural network (BPNN), LSTM, and long-short-term memory optimized by Grey Wolf Optimizer (GWO-LSTM). Root mean square error (RMSE) is reduced by 40.9% to 66.7%;R2score is improved by 1.5% to 7.1%. The experimental results show that CEEMDAN-SE-GWO-LSTM has a higher prediction accuracy than the existing traffic flow prediction models. Finally, this paper uses the model prediction error to establish an interval prediction model based on the kernel density estimation theory, which enhances the generalization of the model and the practical application value.
[Retracted] Empirical Study on the Relationship between Agricultural Economic Structure Growth and Environmental Pollution Based on Time‐Varying Parameter Vector Autoregressive Model
Authors: Li Xiaozhong , Huang Feng
Published: August 2022

In order to better demonstrate the relationship between agricultural economic structure growth and environmental pollution, an autoregressive model based on time‐varying parameter vector was proposed. In the process of developing the research, this paper introduces the LDMI method, based on the time‐varying parameter vector autoregression model, with the help of sampling formula calculation and other methods. Efforts were made to obtain credible conclusions. The experiment result shows that in this study, a total of 10,000 samples were taken. According to this value, 10000/116.15 = 86, which means that at least 86 unrelated samples can be obtained. Therefore, we can determine that each indicator mentioned in this paper has valid samples when it is introduced into the time‐varying parameter vector autoregression (TVP‐VAR) model for parameter estimation. After sampling detection image analysis and data calculation, the effect of energy structure, energy intensity industrial structure, and scale effect on the emission scale of environmental pollutants was obtained. It is proved that through the research of this paper, two main conclusions are finally obtained, and the influence of the five factors mentioned above is summarized.
A Multimodal Fusion Online Music Education System for Universities
Authors: Liu Peng , Cao Yixiao , Wang Lei
Published: August 2022

In the context of Internet technology, the integration of information technology and education is a powerful supplement to the traditional teaching model of higher education. Online learning has become the new development direction of the education industry in the network era. To address the problems of serious difficulty in completing online teaching tasks, difficulty in monitoring teaching effects, and fragmentation of course resources in universities, a multimodal music knowledge graph is constructed. A personalized learning strategy based on users’ interest is proposed through the mining of online education data, and a music online education system has been developed on this basis. To improve the recommendation accuracy of the model, an embedding propagation knowledge graph recommendation method based on decay factors is proposed. The model considers the changes in the strength of user interest during the intra- and interlayer propagation of the knowledge graph interest map and focuses on higher-order user potential interest representations for enhancing the semantic relevance of multihop entities. The experimental results show that the proposed model brings a good prediction effect on several benchmark evaluation metrics and outperforms other comparative algorithms regarding recommendation accuracy.
New Results on the Radial Solutions to a Class of Nonlinear k‐Hessian System
Authors: Wang Guotao , Zhang Zhuobin
Published: August 2022

This paper investigates the positive radial solutions of a nonlinear k‐Hessian system. where Λ is a nonlinear operator and b, h, φ, ψ are continuous functions. With the help of Keller–Osserman type conditions and monotone iterative technique, the positive radial solutions of the above problem are given in cases of finite, infinite, and semifinite. Our results complement the work in by Wang, Yang, Zhang, and Baleanu (Radial solutions of a nonlinear k‐Hessian system involving a nonlinear operator, Commun. Nonlinear Sci. Numer. Simul. 91(2020), 105396).
E-Commerce Personalized Recommendation Model Based on Semantic Sentiment
Authors: Gao Lili , Li Jianmin
Published: August 2022

The real economy has moved to online electronic market transactions as a result of the rapid development of Internet technology. Online shopping makes up a growing portion of transactions in China’s e-commerce market, and the number of users who are aware of online payment transactions on mobile phones is rising. Online shopping platforms like Taobao and JD.com, which are all exemplary online shopping platforms, are constantly emerging. However, because there is so much product information available when shopping online, it can be challenging for users to locate the information they need. Recently developed personalized recommender systems have successfully addressed this issue. The system can predict the user’s preferences through extensive data analysis, and it then pushes the predicted information to the user interface, greatly increasing the user’s purchasing efficiency and the advantages of e-commerce. As a result, in the modern era, research on the personalized recommendation model in e-commerce has become increasingly popular. In this study, semantic sentiment analysis—which is improved on the traditional semantic sentiment analysis algorithm—is introduced in the research of a personalized recommendation system, and 1000 users are chosen for an experimental study. On the user’s personalized product recommendation, the improved semantic sentiment analysis and other widely used personalized recommendation algorithms are compared. According to the survey results, the average transaction success rate is 71.3 percent, and the maximum search time is 1.74 milliseconds when collaborative filtering recommendation algorithm is used. Semantic sentiment analysis has reduced search times to a maximum of 1.42 milliseconds and increased transaction success rates to 87.9 percent. After the addition of semantic sentiment analysis, it is clear that the personalized recommendation system model has a higher accuracy in recommending the products that users have expressed an interest in, which can have a greater positive impact on e-commerce transactions.
[Retracted] Analysis and Research on Influencing Factors of College Students’ Satisfaction with Physical Education
Authors: Liu Xing , Dong Meiyin , Li Yachen , Gao Shanshan , Ye Minghui
Published: August 2022

Physical education is an important part of a university, and the satisfaction of college students for physical education directly determines the teaching effect of physical education. Therefore, it is of great significance to understand college students’ satisfaction with physical education and its influencing factors for improving the level of physical education. In this paper, by means of multistage sampling, probability sampling according to scale and random equidistant sampling, 7 main campuses, including 36 subcampuses, are selected for data entry, cleaning, and calculation by using the college physical education teaching system. Through the investigation of 1752 students, the results show that there are significant differences in grade, gender, cognition, credit, sense of responsibility, and teaching content (P > 0.05), which are all factors affecting college students’ satisfaction. Cognition, grades, credits, and make‐up test rate are the main influencing factors, with the influence degree ranging from 1 to 3, and there are significant differences in OR value and P value. Therefore, in the process of physical education, we should pay attention to the above‐mentioned influencing factors, effectively reduce the occurrence rate of make‐up examination and reexamination, adjust unreasonable teaching content, and improve students’ satisfaction with physical education.
A Genetic Simulated Annealing Algorithm for Real-Time Track Reallocation in Busy Complex Railway Station
Authors: Zeng Qiongfang , Hu Ruihua , Zhang Yinggui , Su Huanyin , Liu Ya
Published: August 2022

Track failure at a railway station is a common disruption in the station area caused by abnormal weather or frequent use. This paper focuses on the real-time track reallocation problem to recover the affected track utilization plan and minimize the total train delays and passenger inconveniences. Train platforming operations in busy complex passenger stations are generally conducted according to fixed track utilization rules. In this paper, we presented a mixed-integer linear programming model for train platforming problems with constraints relevant to fixed track utilization rule and objectives of balanced usage of tracks. Furthermore, we proposed an improved genetic simulated annealing algorithm based on improved crossover and selection methods without breaking the fixed track utilization rule constraint. An experiment of Guanzhou East Station with fixed track utilization rules shows the effectiveness of the proposed model and algorithm. The model and algorithm provide efficient approaches for track reallocation problems based on fixed track utilization rules in busy complex passenger stations.
Site-Specific Fertilizer Recommendation for Barley (Hordeum vulgare L.) Using the QUEFTS Model in Wolaita Zone in Southern Ethiopia
Authors: Kassa Mesfin , Halie Wasie , Kebede Fassil
Published: August 2022

Although Ethiopia is a center of barley domestication and diversity, and barley has an important place in African dry land agriculture due to its resilience to wide-ranging stresses; the average yield continues to be low, mainly due to low soil fertility. Site-specific fertilizer recommendation for barley in Wolaita, Ethiopia, has a pivotal role not only in optimizing barley productivity but also in maintaining ecological balance. Thus, this study was conducted to assess the relationship between grain yield and site-specific nutrient requirements for a target yield using a Quantitative Evaluation of the Fertility of Tropical Soil (QUEFTS) model. The data input was derived from field experiments predominantly in the major barley production areas of Wolaita, Ethiopia. Calibration of the QUEFTS model was estimated by describing the two boundary lines of the maximum accumulation and dilution of N, P, and K contents targeting the barley grain yield. The study revealed that balanced nutrient requirements estimated by the QUEFTS model had a good correlation between the simulated and observed grain yields (r2 = 0.82

∗

, 0.88

∗

, and 0.83

∗

) for different sites, namely, Doga Mashido, Kokate, and Gurimo Koyisha, respectively, while evaluation of the QUEFTS model by U-Theil values showed a good agreement between the simulated and observed yields. Therefore, the study concluded that the QUEFTS model can be used for determining nutrient requirements of crops, thereby contributing to the development of site-specific fertilizer recommendations.
Online English Teaching System Based on Internet of Things Technology
Authors: Tao Hui
Published: August 2022

In order to better improve students’ English performance and adapt to the progress in the age of science and technology faster, the author proposes an online English teaching system method based on Internet of Things technology. The author studies the English SPOC teaching mode and constructs a multimedia teaching system based on the Internet of Things technology, improve the teaching system, and improve and learn the teaching mode, to achieve the improvement of the quality of English teaching. Experimental results show that under the author’s method, students’ scores on both the written and oral exams are about 10 points higher than those in the traditional teaching method. Conclusion. The online English teaching system based on Internet of Things technology can effectively improve students’ English performance and allow students to better control their own progress.
Melatonin Ameliorates the Progression of Alzheimer’s Disease by Inducing TFEB Nuclear Translocation, Promoting Mitophagy, and Regulating NLRP3 Inflammasome Activity
Authors: Fan Li , Zhaohong Xie , Xiangxue Wang , Yingying Xu , Xiao Zhang , Xiaoyan Zhou , Jieke Yan , Chao Lai
Published: August 2022

Background. The NLRP3 inflammasome is overactivated in the brains of APP/PS1 transgenic mice and AD patients, and mitophagy has an obvious negative regulatory role on NLRP3 inflammasome activation. The protective effect of melatonin in AD may be related to the regulation of mitophagy and NLRP3 inflammasome activity. TFEB plays a critical role in maintaining autophagy/mitophagy. Studies have found that TFEB plays a protective role in AD. Methods. APP/PS1 transgenic mice were given melatonin in their drinking water for 3 months. Compared with mice without melatonin treatment, the mice given melatonin showed changes in the following features: (1) cognitive function, (2) mitophagy‐related proteins in the brain, (3) ROS, (4) NLRP3 inflammasome and related proteins and the concentrations of inflammatory cytokines, and (5) Aβ deposition. In in vitro experiments, effects of melatonin on mitophagy, NLRP3 inflammasome activity, and TFEB in SH‐SY5Y cells with Aβ25-35 were observed. TFEB knockdown was implemented in combination with Aβ25-35 and melatonin treatment, and the expressions of TFEB, Parkin, p62, IL‐1β, caspase‐1, ROS, and IL‐18 were explored. Results. Melatonin improved cognitive function in APP/PS1 transgenic mice and decreased ROS and senile plaques. Melatonin promoted mitophagy in SH‐SY5Y cells with Aβ25-35 and APP/PS1 transgenic mice. NLRP3 inflammasome activity was inhibited, and the concentrations of IL‐18 and IL‐1βwere clearly reduced. Compared with C57/BL6J mice, the amount of TFEB in the brain nucleus of APP/PS1 transgenic mice was decreased. Melatonin treatment increased the nuclear translocation of TFEB in SH‐SY5Y cells. TFEB knockout was implemented in combination with Aβ25-35 and MT treatment; the expressions of Parkin, p62, caspase‐1, IL‐1β, IL‐18, and ROS were accelerated. Conclusions. Melatonin promotes mitophagy by inducing TFEB nuclear translocation, downregulates NLRP3 inflammasome activation, and exerts protective effects in SH‐SY5Y cells and APP/PS1 transgenic mice.
Characterizations of Probability Distribution by Some Sequential Relative Reliability Measures: An Application of Completeness in a Hilbert Space
Authors: Shrahili Mansour , Kayid Mohamed
Published: August 2022

In this paper, by means of the concept of completeness in functional analysis, we characterize probability distributions using several relative measures of residual life of an item at independent sequences of random times. These sequences are the order statistics and the record statistics of a random sequence of times. The reliability quantities of the mean residual life, the vitality function, and the survival function are considered to construct the main characteristics of distributions.
Surgical Reduction and Direct Repair Using Pedicle Screw-Rod-Hook Fixation in Adult Patients with Low-Grade Isthmic Spondylolisthesis
Authors: Gao Yongjian , Zhao Chen , Luo Lei , Liu Liehua , Liang Lichuan , Jiang Dianming , Li Pei , Zhou Qiang
Published: August 2022

Background. Although direct pars repair using a pedicle screw-rod-hook system has achieved satisfactory results in patients with spondylolysis, its application in adults with low-grade isthmic spondylolisthesis is rarely reported. Objective. To assess the surgical effect of reduction and direct repair surgery with a pedicle screw-rod-hook system combined with autogenous bone grafts in adult patients with low-grade isthmic spondylolisthesis. Methods. Sixty-four adult patients with low-grade isthmic spondylolisthesis underwent reduction and direct repair using a pedicle screw-rod-hook system in our department from September 2009 to April 2018. The clinical efficacy was evaluated by clinical and radiological assessments. Results. The average follow-up was 52.15 ± 9.96 months. The visual analog scale (VAS) scores (VAS-lumbar and VAS-leg) and Oswestry Disability Index (ODI) at the final follow-up (FFU) were significantly lower than the preoperative levels

P
<
0.05

. The modified Prolo score was “excellent” for 60 patients (93.75%) and “good” for 4 patients (6.25%). The slip distance and slipping percentage showed significant decreases postoperatively and FFU compared to preoperatively

P
<
0.05

. There were no significant differences in the disc height, slip angle, and range of motion of the surgical intervertebral space or upper intervertebral space between preoperation and FFU

P
<
0.05

. Successful bony fusion had a 96.86% success rate. Conclusion. Reduction of slip and direct repair using pedicle screw-rod-hook fixation combined with autogenous iliac bone grafting in adult patients with low-grade isthmic spondylolisthesis is a safe and effective technique.
[Retracted] Analysis of the Efficacy of Acupuncture Combined with Rehabilitation Training in the Treatment of Upper Limb Spasm after Stroke: A Systematic Review and Meta‐Analysis
Authors: Liao Yantan , Liu Fengbin , Yang Jingda , Ma Qishou , Li Junzhe , Chen Yihui , Wu Jiayong
Published: August 2022

Objective. Systematic evaluation of the efficacy of acupuncture combined with cognitive rehabilitation training in the treatment of upper limb spasm after cerebral apoplexy. Methods. The data of CNKI, CBM, CQVIP, Wanfang, and the libraries of Pubmed and Cochrane were searched by computer, and the related literatures about acupuncture combined with cognitive rehabilitation training in the treatment of cognitive dysfunction after stroke were searched. The search time is from January 1, 1995 to January 1, 2022. All data segments were independently analyzed and extracted by two evaluators. After evaluating the quality of the methodology, meta‐analysis was carried out by using the RevMan5.4 software. Results. Finally, 11 studies were included, with a total of 789 subjects. The results of meta‐analysis indicated that acupuncture combined with cognitive rehabilitation training was superior to simple cognitive rehabilitation training or drugs in the following aspects, the difference exhibited statistically significant, the total effective rate (RR = 1.58, 95% CI), latency of P300 (MD = −18.46, 95% CI), amplitude of P300 (MD = 1.23, 95% CI (0.82), P < 0.00001, 95% CI (0.31)), and activity of daily living (ADL), respectively, were significantly higher compared to the control group (P < 0.00001), and the difference was statistically significant (P < 0.05). Based on the results of systematic evaluation, the GRADE system recommendation classification method is used to evaluate the quality of evidence. The results show that the level of evidence is low and the intensity of recommendation is weak. Conclusion. The results of this meta‐analysis suggest that the curative effect of acupuncture combined with cognitive rehabilitation training is better compared to simple cognitive rehabilitation training or drugs. However, due to the low quality of the original literature, it needs to be confirmed by multicenter, high‐quality, large‐sample randomized blind controlled trials in the future.
A Method of Image Semantic Segmentation Based on PSPNet
Authors: Yang Chengzhi , Guo Hongjun
Published: August 2022

Image semantic segmentation is a visual scene understanding task. The goal is to predict the category label of each pixel in the input image, so as to achieve object segmentation at the pixel level. Semantic segmentation is widely used in automatic driving, robotics, medical image analysis, video surveillance, and other fields. Therefore, improving the effect and accuracy of image semantic segmentation has important theoretical research significance and practical application value. This paper mainly introduces the pyramid scene parsing network PSPNet based on pyramid pooling and proposes a parameter optimization method based on PSPNet model using GPU distributed computing method. Finally, it is compared with other models in the field of semantic segmentation. The experimental results show that the accuracy of the improved PSPNet model in this paper has been significantly improved on Pascal VOC 2012 + 2017 data set.