bluuebunny/crossref_metadata_2025 huggingface dataset:
Prevention and Treatment of Sports Injuries and Rehabilitative Physical Training of Wushu Athletes
Authors: Wu Shenghai , Luo Xin
Published: April 2022

This paper is aimed at exploring the characteristics of research on prevention and treatment of sports injuries and rehabilitation physical training of Wushu athletes. It also considers the application of rehabilitation physical training in the rehabilitation of Wushu athletes. By searching literature, conducting questionnaires, and combining mathematical statistics, it studies the injury prevention and rehabilitation training of Wushu athletes. This paper chooses the level of first class and above of sports, and a total of 50 elite male and female Wushu athletes were systematically trained as subjects of study. Athletes, aged 15 to 20 years, were trained for 2 to 5 years, 35 male athletes and 15 female athletes. Different from traditional rehabilitation therapy, athletes’ physical rehabilitation training is also different from traditional sports rehabilitation treatment. By evaluating the physical condition of athletes, the causes of sports injuries were analyzed, to formulate special rehabilitation training programs and carry out athletes’ rehabilitation training targeted and purposeful. Record the experimental data and analyze the experimental results. The experimental results show that physical rehabilitation training can make athletes avoid the influence of unsafe factors of sports injury, improve the safety of training, and effectively prevent sports injury. The experimental results show that physical rehabilitation training combined with rehabilitation medicine has obvious advantages, which can make Wushu athletes recover quickly without sequelae.
Massive Facial Presentation of Dermatofibrosarcoma Protuberans
Authors: Hermans Sebastiaan , Clarysse Karlijn , Las David Emmanuel , de Mey Johan , Mommaerts Maurice Yves , De Brucker Yannick
Published: April 2022

Dermatofibrosarcoma protuberans is a low-grade cutaneous sarcoma typically located on the trunk or proximal extremities. Less common locations include the head, face, and neck area. This tumour is slow growing with variable clinical appearance. It is known for its locally invasive nature and low metastatic propensity. Because imaging findings are rather nonspecific, biopsy is needed for definite diagnosis. This case describes an unusually large example of dermatofibrosarcoma protuberans in the less common preauricular region.
Coordinating a Three‐Level Fresh Agricultural Product Supply Chain considering Option Contract under Spot Price Uncertainty
Authors: Liao Changhua , Lu Qihui
Published: April 2022

We consider a three‐level fresh agricultural product supply chain consisting of a producer, a supplier, and a retailer, where the producer plants the initial fresh agricultural products with yield uncertainty, the supplier is the leader of the supply chain and the designer of the contracts, and the retailer sells processed products with random demand and spot price. This paper discusses coordination mechanism of wholesale price contracts or option contracts between the supplier and the retailer and wholesale price contracts between the supplier and the producer. Based on this, we not only explore the conditions for supply chain full coordination and Pareto improvement but also analyze the effect of the retailer’s spot market volatility by comparing two scenarios of the retailer with or without a random spot market. Results show that the wholesale price contract cannot coordinate the supply chain regardless of whether the retailer has a random spot market or not. The supply chain can be simultaneously fully coordinated and achieve Pareto improvement by setting reasonable option contract parameters, but the conditions for coordination are rigorous. When the expected value of reduction from the retailer’s spot market in unit shortage loss cost is greater, the total profit of supply chain will increase. Numerical analysis shows that the option exercise price must be set higher when the retailer has a spot market to achieve Pareto improvement. The loss rate in distribution process and other volatility risks will also affect the coordination of the agricultural supply chain.
Improved F-DBSCAN for Trip End Identification Using Mobile Phone Data in Combination with Base Station Density
Authors: Jiang Haihang , Yang Fei , Zhu Xin , Yao Zhenxing , Zhou Tao
Published: April 2022

Trip end identification based on mobile phone data has been widely investigated in recent years. However, the existing studies generally use fixed clustering radii (CR) in trip end clustering algorithms, but ignore the influence of base station (BS) densities on the positioning accuracy of mobile phone data. This paper proposes a new two-step method for identifying trip ends: (1) Genetic Algorithm (GA) is utilized to optimize the CRs of DBSCAN under different BS densities. (2) We propose an improved Fast-DBSCAN (F-DBSCAN) for two objectives. One is for improving identification accuracies; the parameter CRs for judging core points can be dynamically adjusted based on the BS density around each mobile phone trace. The other is for reducing time complexity; a fast clustering improvement for the algorithm is proposed. Mobile phone data was collected by real-name volunteers with support from the communication operator. We compare the identification accuracy and time complexity of the proposed method with the existing ones. Results show that the accuracy is raised to 85%, which is approximately 6% higher than the existing methods. Meanwhile, the median running time can be reduced by about 76% by the fast clustering improvement. Especially for noncommuting trip ends, the identification accuracy can be increased by 8%. The average identification errors of travel time and trip end coordinates are reduced by about 12 min and 321 m, respectively.
Paths Study on Knowledge Convergence and Development in Computational Social Science: Data Metric Analysis Based on Web of Science
Authors: Liu Yuxi , Feng Xin , Zhang Yue , Kong Ying , Yang Rongyao
Published: April 2022

Computational social science, as an emerging interdisciplinary discipline, is a field ushered in by long‐term development of traditional social science. It is committed to supplying data thinking, resources, and analytics to study human social behavior and social operation laws to accurately grasp and judge the developing path of the discipline, which is of great significance to promote the innovation and development of social sciences. This study is to conduct a systematic quantitative analysis from a bibliometric perspective, aiming to provide a reference for scholars to explore the paths and changing rules in the field. We use the relevant literature in Web of Science as the dataset. After eliminating journal calls and irrelevant literature, R language and SciMAT tools are used to visualize and analyze the number of articles, keyword clustering, keyword cooccurrence network, and theme evolution, so as to summarize and sort out the paths of computational social science research. The study found that the annual volume of publications has been gradually increasing and will probably remain active in the next few years with high productivity. Subject themes in different periods are diversified, and the evolutionary relationship is found complex as well. Besides, as a cross discipline, scientific knowledge from different fields cross collides and couples with each other in the big data environment, changing the traditional concept of computational social science and forming a new development path. Recently, the emergence of “big data+” has promoted the rise of new subject areas, making the development of new disciplines a reality.
Correlation of Serum IL-18, BDNF, and IL-1β with Depression and Prognosis after Acute Exacerbation of Chronic Obstructive Pulmonary Disease
Authors: Zhao Nan , Dong Chengcheng
Published: April 2022

Objective. To explore the correlation of serum IL-18, BDNF, and IL-1β with depression and prognosis after acute exacerbation of chronic obstructive pulmonary disease (COPD). Methods. By means of retrospective analysis, the data of 240 patients at the acute exacerbation of COPD treated in our hospital (February 2018-February 2021) were analyzed. All patients received conventional treatment 1 d after admission, patients’ serological indicators were measured before treatment, and after 30 d of follow-up, the patients were divided into the survival group (SG) and death group (DG) according to their clinical outcomes, the Beck’s Depression Inventory (BDI) scores of the surviving patients were investigated, the correlation of IL-18, BDNF, and IL-1β levels with depression was analyzed by R analytics, and the correlation of IL-18, BDNF, and IL-1β levels with prognosis was analyzed by ROC curve analysis. Results. The results of 30 d follow-up showed that 220 patients survived (91.7%) and 20 patients died (8.3%). Among the surviving patients, 95 patients had depression and 125 patients did not have depression; the BDI scores of the depressed subjects and the nondepressed subjects were

10.35
±
1.25

points and

2.06
±
0.76

points, respectively; significant differences in IL-18, BDNF, and IL-1β levels between SG and DG were observed (

P
<
0.05

); significant differences in IL-18, BDNF, and IL-1β levels between the depressed subjects and the nondepressed subjects were observed (

538.43
±
19.02

vs.

515.32
±
9.65

,

7.54
±
0.56

vs.

12.11
±
2.41

, and

8.70
±
0.98

vs.

8.12
±
0.87

;

P
<
0.001

); among the depressed patients, the IL-18 and IL-1β levels were positively correlative with the BDI scores (

r
=
0.781

,

r
=
0.2583

,

P
<
0.001

,

P
=
0.012

), and the BDNF level was negatively correlative with the BDI scores (

r
=
−
0.3277

,

P
=
0.001

) before treatment; according to the ROC analysis, the AUC (95% CI) of IL-18, BDNF, and IL-1β in predicting prognosis was 0.8770 (0.8281-0.9260), 0.7723 (0.6879-0.8567), and 0.7165 (0.6080-0.8250) (

P
<
0.05

), respectively. Conclusion. In regard to the depression in COPD patients after acute exacerbation, IL18 and IL-1β show positive correlation, and BDNF presents negative correlation. All three indicators have predictive value for patient outcome.
Creation Skills of Perspective Effect Drawing of Garden Landscape Based on CAD Technology
Authors: Zheng Haixia , He Jin
Published: April 2022

In order to enable more garden landscape design enthusiasts to master the creation skills of perspective renderings, this article introduces CAD computer-aided technology in the creation process of perspective renderings of garden landscapes. In the research, through case analysis, the CAD technology is compared with the commonly used sketch master SketchUp, and the detailed steps of making the garden landscape perspective renderings for small-scale modeling of garden landscapes are compared in detail. Finally, an example analysis shows that the application of CAD technology to the creation of perspective renderings of garden landscape can make the creation process more scientific, reasonable, and more ornamental.
A Deep Learning-Based Framework for Social Data Sensing and Fusion for Enterprise Management
Authors: Wang Yu
Published: April 2022

How to effectively realize the perception and fusion of data in the enterprise management society is the core problem that must be solved by enterprise management. On the basis of defining the resource system of enterprise management society, a semantic-oriented metadata model is proposed, combined with the classification of user needs, to build a data fusion framework for enterprise management society based on multisource data. The study designs a multilayer convolutional neural network model to process the data and proposes a recommendation path for the implementation of user data services for the enterprise management society based on the data operation center of the enterprise management society. Finally, it proposes suggestions for the development of the data fusion of the enterprise management society by improving the data fusion standard of the enterprise management society from multiple sources, actively formulating the data opening policy, and exploring the personal data collection and storage protection scheme. Experiments show that the scheme designed in this study is 5% more accurate than the state-of-the-art scheme.
Internet Rumor Audience Response Prediction Algorithm Based on Machine Learning in Big Data Environment
Authors: Yang Suhong , Wang Shenghui , Yiwen Y.
Published: April 2022

Rumors are an important factor affecting social stability in some special times. Therefore, the dissemination and prevention and control mechanisms of rumors have always been issues of concern to the academic community and have long been highly valued and widely discussed by experts and scholars. However, in combination with the Internet as a new type of media, although people have begun to pay attention to online rumors, research on it is still relatively fragmented, especially in the cross-domain research specific to the social influence of online rumors, and there is no clear indication of online rumors. The specific definition also did not analyze in detail the internal connection between its influence and group behavior. Therefore, this article will combine actual cases to explore and analyze the spread and influence process of online rumors and show its social influence, hoping to enrich the research of online rumors. Nowadays, the Internet has become the most important carrier to reflect the public grievances. Internet users have expressed their opinions on hot issues such as enterprises, people’s livelihood, and government management, which has formed a powerful public opinion pressure, which has far exceeded the traditional media. The hidden dangers of security cannot be ignored. Therefore, how to monitor network public opinion from a large amount of network data is a difficult problem that needs to be solved urgently. Firstly, this consists of four modules: information collection, web page preprocessing, public opinion analysis, and public information report. Secondly, text clustering, the core technology of network public opinion, is optimized, and single-pass algorithm based on double threshold is proposed. Then the dual-threshold single-pass algorithm is optimized based on the MapReduce parallel computing model, and finally a network public opinion collection technology is formed under the background of big data. Simulation results can greatly improve the performance of text clustering and can effectively optimize the design using the parallel computing model based on MapReduce. The average miss rate after optimization is 0.7569 times, the average false alarm rate is 0.5556 times, and

C

det

is 0.5714 times. It proves that the collection technology based on machine learning under the background of big data is effective and has good performance.
Research on Athlete Training Effect Evaluation Based on Machine Learning Algorithm
Authors: Zou Yan , Wang Chu , Jiao Qianqian
Published: April 2022

In order to achieve a quantitative analysis of the training effects of athletes, this paper combines machine learning algorithms to evaluate and analyze athletes’ training effects and analyzes the evaluation algorithms based on machine learning interpretable models. Moreover, after analyzing a variety of algorithms, this paper selects an intelligent evaluation method suitable for this model and builds an intelligent evaluation system based on the current athletes’ training needs. In addition, this paper verifies the effect of the system with the support of intelligent algorithms and experiments. The experimental research results show that the athlete training effect evaluation system based on the machine learning algorithm proposed in this paper has good results, and it can be applied to subsequent athletes’ sports training evaluation.
Purity Effect of High-Strength Steel on Failure Behavior of Automobile Front Axle
Authors: Xue Huan , Huang ShiYao , Zhu Min , Wang Zhifen , Liu Shengnan , Zhang Yansong
Published: April 2022

As unpredictable fracture of the front axle will lead to serious safety accidents, its failure behavior study is essential. This paper analyzes typical cracks on I-shaped and leaf springs of the front axle made by Steel A and Steel B. Steel B has a lower content of S, N, Cu, and Sn than Steel A. In order to identify the failure mechanism difference, the low magnification etch test, and metallographic and scanning electron microscope analyses were conducted. The results show that the microstructures are tempered sorbite, and the cracks are with intergranular fracture characteristics. The aggregated distribution of sulfide inclusions in the matrix of Steel A is higher than that of Steel B. Copper-rich particles are detected in the cracks of Steel A. The result indicates that the purity distinction of raw steel can have a big difference on the cracking rate of the front axle even under the same manufacturing process. Relevant suggestions to reduce the cracking are put forward from the aspects of raw materials and process conditions.
Lightweight Design of Special Axle Hub Based on ANSYS Consider Different Working Conditions for Low Carbon
Authors: Zhang Cuixia , Li Qiang , Zhang Xiuxiang , Wang Xinyu , Chen Jianqing , Shen Xuehong
Published: April 2022

The lightweight design is considered to be of great significance for carbon reduction. Therefore, a lightweight design of a special axle hub is proposed based on ANSYS considering different working conditions. The structure of a special axle hub is identified. The ANSYS Workbench is used to mesh the hub and import the mechanical and physical parameters. Considering full load, emergency turning and braking, and the treatment of constraints and loads, the stress concentration position is obtained, and the lightweight design is carried out. The free vibration analysis and fatigue life prediction of the hub after lightweight design are verified. After the lightweight design, the optimized hub mass is reduced by 7.52%, and its structural stability and strength meet the demands of various working conditions. The application of a lightweight hub reduces CO2 emissions by 49875 kg during the life cycle of the vehicle. This study provides theoretical and methodological support for lightweight design of mechanical parts for low carbon.
A New Code-Based Traceable Ring Signature Scheme
Authors: Qi Yanhong , Wang Li-Ping
Published: April 2022

Traceable ring signatures (TRS) can reveal the identity of the signer if he signs two different messages on the same tag in the group of users. They are widely used in e-voting and cryptocurrencies such as Monero. However, there is still no secure code-based TRS scheme in the random oracle model (ROM). In this paper, we propose a code-based TRS scheme whose security is based on the hardness of the syndrome decoding problem and 2-regular null syndrome decoding problem. We show that our scheme is secure in the ROM in terms of tag-linkability, anonymity, and culpability. The signature size of our scheme is logarithmic in terms of the ring size.
Situated Information Flow between Food Commodity and Regional Equity Markets: An EEMD‐Based Transfer Entropy Analysis
Authors: Agyei Samuel Kwaku , Owusu Junior Peterson , Bossman Ahmed , Arhin Emmanuel Yaw
Published: April 2022

The intrinsic information shared by financial assets provides a means of assessing their mutual linkages. In times of crisis, spillovers and information flow between markets increase, and this drives empirical investigations into the degree of connectedness between financial assets. In the context of commodity markets, empirical evidence about the mutual information shared and its influence on portfolio management is largely unknown. This study examines the situated information between the food commodities (cereals, dairy, food, meat, vegetable oil, and sugar) of the FAO and regional stock markets’ returns. From the ensemble empirical mode decomposition (EEMD)‐based Rényian transfer entropy analysis employed, we find significant bidirectional information flow between the food commodities and regional equity markets. Our findings divulge that the diversification potentials of food commodities rest in the long term, with sugar being a consistent diversifier across all investment horizons. The investment and policy implications of our findings are further discussed.
Stability Prediction of Residual Soil and Rock Slope Using Artificial Neural Network
Authors: Paliwal Mahesh , Goswami Himkar , Ray Arunava , Bharati Ashutosh Kumar , Rai Rajesh , Khandelwal Manoj
Published: April 2022

A sudden downward movement of the geomaterial, either composed of soil, rock, or a mixture of both, along the mountain slopes due to various natural or anthropogenic factors is known as a landslide. The Himalayan Mountain slopes are either made up of residual soil or rocks. Residual soil is formed from weathering of the bedrock and mainly occurs in gentle‐to‐moderate slope inclinations. In contrast, steep slopes are mostly devoid of soil cover and are primarily rocky. A stability prediction system that can analyse the slope under both the condition of the soil or rock surface is missing. In this study, artificial neural network technology has been utilised to predict the stability of jointed rock and residual soil slope of the Himalayan region. The database for the artificial neural network was obtained from numerical simulation of several residual soils and rock slope models. Nonlinear equations have been formulated by coding the artificial neural network algorithm. An android application has also been developed to predict the stability of residual soil and rock slope instantly. It was observed that the developed android app provides promising results in predicting the factor of safety and stability state of the slopes.
Design of Rural Human Resource Management Platform Integrating IoT and Cloud Computing
Authors: Chai Meng
Published: April 2022

With the advent of the Internet of Things era, these hot technologies such as distributed, parallel computing, network storage, and load balancing can provide a good application foundation for the Internet of Things, enabling real-time dynamic management and intelligent analysis of hundreds of millions of items in the Internet of Things to be possible. The Internet of Things has changed from a concept to a reality, quickly reaching every corner of society. On the other hand, with the enhancement of the mobility of social talents, the file management of the talent service center is becoming more and more difficult. However, the traditional management methods of human resources files have problems such as poor resource sharing, asymmetric resources, and heterogeneous information sharing, which can no longer meet the needs of both the supply and demand sides of human resources with diversified and multiple organizational structures. Cloud computing technology has powerful data collection functions, self-service functions, and unified resource scheduling functions. Introducing it into the human resources file management system can greatly improve management efficiency. In order to carry out information management of rural human resources, this paper develops a rural human resources management system based on the Internet of Things. This paper introduces the design scheme of rural human resource management platform based on Internet of Things technology and cloud computing technology. The design of this system mainly includes organization setting, post planning, personnel management, salary management, insurance benefits, recruitment and selection, training management, performance appraisal management, labor contract management, comprehensive inquiry, rules and regulations, employee self-help, system setting, and system management function modules. The research results show that the rural human resource management system based on cloud computing can provide a complete human resource management solution for the vast rural areas. It can only purchase services, save a lot of development and maintenance costs, and also customize functions, so as to better meet the needs of use.
On Shape Parameter α‐Based Approximation Properties and q‐Statistical Convergence of Baskakov‐Gamma Operators
Authors: Chen Ming-Yu , Nasiruzzaman Md , Mursaleen Mohammad Ayman , Rao Nadeem , Kilicman Adem
Published: April 2022

We construct a novel family of summation‐integral‐type hybrid operators in terms of shape parameter α ∈ [0,1] in this paper. Basic estimates, rate of convergence, and order of approximation are also studied using the Korovkin theorem and the modulus of smoothness. We investigate the local approximation findings for these sequences of positive linear operators utilising Peetre’s K‐functional, Lipschitz class, and second‐order modulus of smoothness. The approximation results are then obtained in weighted space. Finally, for these operators q‐statistical convergence is also investigated.
Convolutional Neural Network-Based Cross-Media Semantic Matching and User Adaptive Satisfaction Analysis Model
Authors: Jiang Lanlan
Published: April 2022

In this paper, an in-depth study of cross-media semantic matching and user adaptive satisfaction analysis model is carried out based on the convolutional neural network. Based on the existing convolutional neural network, this paper uses rich information. The spatial correlation of cross-media semantic matching further improves the classification accuracy of hyperspectral images and reduces the classification time under user adaptive satisfaction complexity. Aiming at the problem that it is difficult for the current hyperspectral image classification method based on convolutional neural network to capture the spatial pose characteristics of objects, the problem is that principal component analysis ignores some vital information when retaining a few components. This paper proposes a polymorphism based on extension Attribute Profile Feature (EMAP) Stereo Capsule Network Model for Hyperspectral Image Classification. To ensure the model has good generalization performance, a new remote sensing image Pan sharpening algorithm based on convolutional neural network is proposed, which increases the model’s width to extract the feature information of the image and uses dilated instead of traditional convolution. The experimental results show that the algorithm has good generalization while ensuring self-adaptive satisfaction.
Holistic Language Teaching Method in College English Vocabulary Teaching under Big Data and Multimedia Environment
Authors: Peng Xiaoxia
Published: April 2022

With the rapid advancement of informatization on a global scale, human society has entered a new era of information explosion and continuous data updating. In this situation, big data have become the future development trend of the world. In the context of big data, we must not only understand the world but also learn how to obtain information, and traditional teaching models can no longer meet the needs of teaching. Therefore, it is necessary to combine modern information technology, multimedia technology, and other means to assist the development of classroom teaching. At the same time, the holistic language teaching method plays an important role in improving students’ comprehensive ability, comprehension, expression, and thinking innovation ability. Therefore, this article attempts to combine the teaching background of big data and multimedia and use their advantages in teaching to find effective teaching methods to promote students’ vocabulary acquisition. This article uses questionnaire surveys and data analysis methods to understand the difficulties encountered by college students in English vocabulary learning and their views on the application of holistic language teaching methods in English vocabulary teaching. According to the survey results, due to factors such as large English vocabulary and complicated spelling rules, most interviewees think English vocabulary learning is more difficult. In addition, most of the interviewees believe that the application of the whole language teaching method in college English vocabulary teaching is very necessary and will get better teaching results, thereby improving the efficiency of college students in learning English vocabulary.
Insight into the Vacuolar Compartmentalization Process and the Effect Glutathione Regulation to This Process in the Hyperaccumulator Plant Solanum nigrum L.
Authors: Li Zhishuai , Guan Wenjie , Yang Lan , Yang Yan , Yu Hongyan , Zou Luyi , Teng Yue
Published: April 2022

Vacuole compartmentalization plays an important role in the storage of heavy metals in hyperaccumulators. Is the vacuolar compartmentation a simple shielding process or a dynamic process that continuously consumes cell sap resources? How does glutathione affect the process of vacuolar compartmentalization? These unknown questions are very important to understand the mechanism of vacuole compartmentalization and can provide a guide for the design of hyperaccumulator plants by genetic engineering. Therefore, this study explored the enzyme activities, total cadmium, Cd2+, glutathione, oxidized glutathione, and reactive oxygen species contents in protoplasts and vacuoles of leaf cells in Solanum nigrum L. through subcellular separation. The results showed that vacuolar compartmentalization was a dynamic process that actively induced the related substances produced by cell sap to enter the vacuole for detoxification. When regulating the decreased glutathione content with buthionine sulfoximine, the total cadmium and combined cadmium in protoplasm decreased significantly, but the vacuole still maintained a high proportion of cadmium content and stable ROS content, which indicated that various external resources were preferentially used to maintain cadmium storage and homeostasis in vacuole rather than outside vacuole. These findings could guide the use of genetic engineering to design hyperaccumulator plants.
N‐Lump to the (2+1)‐Dimensional Variable‐Coefficient Caudrey–Dodd–Gibbon–Kotera–Sawada Equation
Authors: Li Junjie , Manafian Jalil , Wardhana Aditya , Othman Ali J. , Husein Ismail , Al-Thamir Mohaimen , Abotaleb Mostafa
Published: April 2022

In this research, the (2 + 1)‐dimensional (D) variable‐coefficient (VC) Caudrey–Dodd–Gibbon–Kotera–Sawada model used in soliton hypothesis and implemented by operating the Hirota bilinear scheme is studied. A few modern exact analytical outcomes containing interaction between a lump‐two kink soliton, interaction between two‐lump, the interaction between two‐lump soliton, lump‐periodic, and lump‐three kink outcomes for the (2 + 1)‐D VC Caudrey–Dodd–Gibbon–Kotera–Sawada equation by Maple Symbolic packages are obtained. By employing Hirota’s bilinear technique, the extended soliton solutions according to bilinear frame equation are received. For this model, the contemplated model can be got by multi‐D binary Bell polynomials (bBPs). In addition, the analytical analysis of the high‐order soliton outcomes to present the discipline of outcomes. The effect of the free parameters on the behavior of acquired figures of a few obtained solutions for the nonlinear rational exact cases was also discussed. The above technique could also be employed to get exact solutions for other nonlinear models in physics, applied mathematics, and engineering.
PCMT1 Is a Potential Prognostic Biomarker and Is Correlated with Immune Infiltrates in Breast Cancer
Authors: Guo Jufang , Du Xuelian , Li Chaolin
Published: April 2022

Background. Protein‐L‐isoaspartate (D‐aspartate) O‐methyltransferase (PCMT1) is involved in the occurrence and development of a variety of malignant tumors. However, the prognostic value of PCMT1 in breast cancer remains unclear. Methods. Based on the Cancer Genome Atlas database, we assessed the correlation between the expression of PCMT1 and prognosis, immune invasion, and tumor mutation burden in a variety of cancers. The expression level, mutation, immune correlation, and coexpression of PCMT1 in breast cancer were studied using the following databases: UALCAN database, Human Protein Atlas database, cBioPortal database, TIMER database, and LinkedOmics database. Kaplan–Meier Plotter was used for survival analysis. Receiver operating characteristic (ROC) curves and nomograms were drawn using the R software package. P < 0.05 was considered statistically significant. Results. Pancancer analysis showed that PCMT1 is highly expressed in a variety of cancers and is significantly related to the prognosis of a variety of cancers. PCMT1 is significantly related to the tumor mutation burden of a variety of cancers. PCMT1 is significantly high in breast cancer, and it is significantly related to the abundance of immune infiltration. Survival analysis revealed that high PCMT1 expression is significantly associated with shorter overall survival (OS), relapse‐free survival (RFS), and postprogression survival (PPS) in breast cancer patients. ROC curves and nomograms verify the effectiveness of PCMT1 as a prognostic biomarker for breast cancer. Conclusions. PCMT1 can be used as a potential prognostic biomarker of breast cancer, and it is significantly related to the abundance of breast cancer immune infiltration.
A Review in the Automatic Detection of Pigs Behavior with Sensors
Authors: Zhang Ziting , Zhang Hang , He Yuxiang , Liu Tonghai
Published: April 2022

In the trend of large-scale and intensive livestock farming, it has become difficult to monitor the physical health and breeding performance of animals accurately, quickly, and comprehensively in the traditional feeding management mode, and with the development of computer technology, machine vision has been widely used as a non-contact sensor technology. In this work, it reviews the current automatic behavior detection methods. The chapters are summarized with machine vision technology as the core and classical behavior detection techniques are added for comparison. It is classified into three aspects: breeding performance, disease and health, and social behavior, and then discusses in detail daily behaviors (lying, drinking, feeding, lameness, etc.) and complex behaviors (mounting, aggression, tail biting, etc.).
Python‐Based Visual Classification Algorithm for Economic Text Big Data
Authors: Jiang Yihuo , Guo Xiaomei , Ni Hongliang , Jiang Wenbing
Published: April 2022

In order to improve the classification accuracy and reduce the classification time of the economic text big data visualization classification algorithm, one based on the Pitton algorithm is proposed. The economic text big data are preprocessed by filtering out useless symbols, word segmentation processing, and removing stop words. According to the processing results, the most relevant features of the economic text big data classification process are selected, including Gini index, information gain, mutual information, etc., and the TF‐IDF weighting algorithm is used to weight the economic text data features. Based on feature weighting, using Naive Bayesian algorithm, combining classification probability distribution and text probability distribution, Naive Bayesian classifier is constructed to obtain the optimal classification result through input vector, and the visual classification of economic text big data is completed through Python software programming. The simulation results show that the classification accuracy of the algorithm for the visual classification of economic text big data can reach 100%, and the classification time is less than 5 seconds. It has high accuracy and fast efficiency.
Modified Harris Hawks Optimization Algorithm with Exploration Factor and Random Walk Strategy
Authors: Song Meijia , Jia Heming , Abualigah Laith , Liu Qingxin , Lin Zhixing , Wu Di , Altalhi Maryam
Published: April 2022

One of the most popular population-based metaheuristic algorithms is Harris hawks optimization (HHO), which imitates the hunting mechanisms of Harris hawks in nature. Although HHO can obtain optimal solutions for specific problems, it stagnates in local optima solutions. In this paper, an improved Harris hawks optimization named ERHHO is proposed for solving global optimization problems. Firstly, we introduce tent chaotic map in the initialization stage to improve the diversity of the initialization population. Secondly, an exploration factor is proposed to optimize parameters for improving the ability of exploration. Finally, a random walk strategy is proposed to enhance the exploitation capability of HHO further and help search agent jump out the local optimal. Results from systematic experiments conducted on 23 benchmark functions and the CEC2017 test functions demonstrated that the proposed method can provide a more reliable solution than other well-known algorithms.
Experimental Study on the Influence of Type of Stirrup of Prestressed Concrete Beams with Corroded Rebars
Authors: Li Fang-Yuan , Li Liu-Yang , Li Wen-Hao , Wu Peifeng
Published: April 2022

Corrosion of longitudinal reinforcement and stirrup in reinforced and prestressed concrete beams is very common for structures and infrastructures, which is one of the main reasons for the decrease in stiffness and durability of structure. In order to evaluate the influence of corrosion of different types of stirrups on the bearing capacity of the prestressed concrete beam, the method of using electricity to accelerate corrosion was adopted. Load experiment on 4 corroded prestressed concrete beams with different corrosion rates and different stirrup types was carried out. In this paper, reduced scale model beams were designed based on the 5 × 50 m simple‐continuous box girder of the Donghai Bridge. The results show that for prestressed concrete beams with different corrosion rates and stirrup types, the load‐deflection curve can be divided into an elastic stage and a plastic stage. The corrosion of longitudinal bars and stirrups will reduce the flexural capacity, but the overall decline is not obvious. When the corrosion rate is 10%, the bearing capacity of the ordinary stirrup specimen decreases by 6.1%; epoxy‐coated stirrup specimen decreases by 4.9%; stainless stirrup specimen has nearly no change. The influence of different types of stirrups on cracking load, deflection, and bond‐slip behavior is discussed.
Adaptive Grid Division and Cluster Analysis of Decision Graph in Real Estate Marketing Using Data Mining
Authors: Lv Sheng
Published: April 2022

China’s economy has been in a state of rapid development in recent years. People’s lifestyles and quality of life have also undergone earth-shaking changes. More people put forward requirements for the living environment and living environment, which promotes the rapid rise of the real estate industry. The state has promulgated several macrocontrol measures. Real estate companies analyze the needs of various customer groups, affordable housing unit prices, and family populations, and formulate real estate marketing strategies to improve real estate sales. Therefore, this paper practices data mining technology to precisely analyze and locate the target customers. The cluster analyzes the mapping of the real estate marketing bureau. The cluster further divides the adaptive network and selects the data of a real estate company in China for research. It also analyzes the relationship between the family population and the affordable housing unit price and comprehensively improves the accuracy of real estate marketing. The results show that the acceptable housing unit price for families in this area is 9800 yuan. Study its data characteristics to judge the relationship between the population of other families. The analysis and research of data mining technology in the context of real estate can formulate more accurate marketing strategies. Moreover, it can reduce the investment amount of real estate companies in marketing.
Quality of Evidence Supporting the Effects of Tai Chi Exercise on Essential Hypertension: An Overview of Systematic Reviews and Meta-Analyses
Authors: Shi Hongshuo , Wu Zixuan , Wang Dan , Dong Chengda , Liu Pulin , Si Guomin , Liu Ting
Published: April 2022

Objectives. Tai Chi (TC) is a potential complementary treatment for essential hypertension (EH). This overview systematically summarizes and evaluates the existing evidence of TC in the therapy of EH. Methods. Systematic reviews (SRs)/meta-analyses (MAs) on TC interventions for EH were comprehensively searched in seven databases. Methodological quality, risk of bias, reporting quality, and quality of evidence were assessed by means of the Assessment of Multiple Systematic Reviews 2 (AMSTAR-2), the Risk of Bias in Systematic (ROBIS) scale, the list of Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA), as well as the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system. Results. Twelve published SRs/MAs were included in our study. According to the results of the AMSTAR-2, ROBIS, PRISMA, and GRADE assessment, only 1 SR/MA was assessed as high quality and only 1 SR/MA was assessed as low risk of bias. Only 2 SRs/MAs have been fully reported on the checklist. In addition to that, the quality of evidence was assessed for a total of 69 outcome indicators extracted from the SRs/MAs included in this overview, and only 3 items were assessed as high quality. Conclusions. TC may be an effective and safe complementary treatment for EH. However, this conclusion must be approached with caution, as the quality of the evidence provided by the SRs/MAs is usually low.
Retrieval Algorithm of Water Pollutant Concentration Based on UAV Remote Sensing Technology
Authors: Ling Min , Cheng Qun , Peng Jun , Jiang Ling , Wang Ruifeng
Published: April 2022

With the development of the society and economy, traditional water pollution monitoring methods can no longer meet the normal needs of work. Unmanned aerial vehicle remote sensing technology has gradually emerged, and it has shown a development trend of multimodel and multifunction. However, the application of UAV remote sensing technology in water pollution monitoring is in its infancy and has not formed a unified method and standard. This paper introduces the disadvantages of UAV Remote Sensing Technology in water pollution monitoring and provides a way to improve the application level of UAV Remote Sensing Technology in water pollution monitoring. In order to make full use of the advantages of remote sensing monitoring technology in water ecological environment monitoring, including wide coverage, fast analysis speed, accurate and reliable monitoring results, and large amount of information, according to the spectral effect of polluted water, a remote sensing inversion model of river and lake parameters is established to determine the water quality of rivers and lakes. Concentrations of pollutants in the body are inverted and analyzed. The analysis results show that the fitting effect of the inversion model is accurate and the accuracy errors reflected by the degree of dispersion and deviation are also acceptable. The inversion results of the concentration of river and lake characteristic parameters based on the Gaofen-1 remote sensing image data are in line with the actual situation. The research results can greatly strengthen the monitoring capacity of the sewage outlet and provide reference for related research on water pollution caused by the sewage outlet.
Influencing Factors of College Students’ Sports Cognition Based on DM Technology
Authors: Pan Qingwen
Published: April 2022

College students are the pillars of the country’s future construction, shouldering the historical mission of building the motherland and rejuvenating the nation, forming a good habit of scientific exercise during school, and laying a good physical condition for social work in the future. When their life is more closely related to sports, they will have higher and more specific requirements for the use of sports. In college students’ sports cognition, the main influencing factors are to improve athletes’ competitive level. The most direct and effective way is to strengthen training. China’s DM technology is the concentrated expression of multidisciplinary knowledge, and the scope of technology is also relatively wide. At this stage, China’s DM technology adopts many extremely intuitive expressions to convey the beauty of technology. For example, the tree structure is a special expression of DM technology. This article studies the influencing factors of college students’ sports cognition based on data mining (DM) technology. What is more related to China’s DM technology is the unique regularization of the influencing factors of college students’ sports cognition. To a large extent, this trend is the data standard of China’s sports industry. It can be seen that China’s DM industry also has the characteristics of diversification. According to the characteristics of China’s DM technology, the current advanced expenses are enough to supplement the related technical weaknesses, and further technical strengthening can be carried out on this basis so that China’s DM technology will gradually develop towards specialization.
A Novel Controller Design for Small-Scale Islanded Microgrid Integrated with Electric Vehicle-Based Energy Storage Management
Authors: Omar Nihal , Kumar Tiwari Anil , Seethalekshmi K. , Anand Shrivastava Nitin
Published: April 2022

Small-scale islanded microgrid technology has the potential to utilize renewable sources for electrification in extreme areas where conventional transmission of power is difficult. But due to the absence of a utility grid in the islanded operation, voltage and frequency control becomes the major concern. Instead of using a centralized large battery storage system, electric vehicle- (EV-) based distributed energy storage may provide a dynamic and much cheaper energy storage solution for small-scale systems in the long run. The only issue is to effectively manage the dynamics of EV penetration for achieving the overall control in the islanded system. To address the same issue, this paper proposes a novel segregation-based inverter control structure for an islanded microgrid while employing EVs as an energy storage system, thus eliminating the need for centralized storage. The designed integrated control structure simultaneously attends to the voltage and frequency regulation need of the system, overcoming the issue of control lag, along with energy storage and management aspects in the microgrid operation for controlling the power flow under various practical scenarios. Extensive experimental studies are performed in MATLAB/SIMULINK environment, which indicates that the proposed integrated control structure gives a satisfactory performance under various scenarios encompassing load variation, renewable energy uncertainties, and EV dynamics in comparison with conventional control schemes employed in the islanded microgrid.