Mathematical Problems in Engineering

Published by Hindawi
Online ISSN: 1563-5147
Discipline: Applied Mathematics In Engineering
Learn more about this page
Aims and scope

Mathematical Problems in Engineering is a broad-based journal which publishes articles of interest in all engineering disciplines. Mathematical Problems in Engineering publishes results of rigorous engineering research carried out using mathematical tools. Contributions containing formulations or results related to applications are also encouraged. The primary aim of Mathematical Problems in Engineering is rapid publication and dissemination of important mathematical work which has relevance to engineering. All areas of engineering are within the scope of the journal. In particular, aerospace engineering, bioengineering, chemical engineering, computer engineering, electrical engineering, industrial engineering and manufacturing systems, and mechanical engineering are of interest. Mathematical work of interest includes, but is not limited to, ordinary and partial differential equations, stochastic processes, calculus of variations, and nonlinear analysis.



Recent publications
  • Vladimir ModrakVladimir Modrak
  • R. Sudhakara PandianR. Sudhakara Pandian
  • Ponnusamy VenkumarPonnusamy Venkumar
  • Chrysostomos StyliosChrysostomos Stylios
  • YiLin ShaoYiLin Shao
  • HanNing LuHanNing Lu
  • QingYuan LiuQingYuan Liu
  • GaoShuo LiGaoShuo Li
Since the 3rd technological revolution, electronic information has developed at an increasingly rapid pace and has been widely used in various aspects of people’s lives and social production. With the advent of the information society, information security is particularly important and is related to the security of various industries and fields, such as transportation, national defense, and economy. If there is a problem in information, it means that there is a problem in information countermeasures, which leads to the emergence of information countermeasures technology. At present, with the accelerated progress of socialization, the requirements for information technology talents have been raised accordingly, so schools are required to pay attention to the education and training of talents in this area to meet the needs of society and speed up the development of society. Moreover, more and more scholars are recognizing the significance of data analysis technology for education development, and some scholars have constructed learning prediction models from different educational environments and perspectives. However, some of the models have their own limitations and thus can hinder the parameter setting. Therefore, educational researchers need to combine the characteristics of their own educational environment to build a widely adaptable predictive model to provide a good foundation for the development of education. Also, on that basis, information confrontation technology should be applied to explore diverse teaching courses, improve the traditional teaching philosophy, improve the management and evaluation mechanism, provide students with diverse learning styles, emphasize the practical nature of learning, and continuously improve students’ independent problem-solving ability.
  • Hengyao TangHengyao Tang
  • Qingdong WangQingdong Wang
  • Guosong JiangGuosong Jiang
With the popularization of the Internet, various online learning platforms have developed rapidly, providing users with abundant learning resources, and realizing personalized resource recommendation has become the development trend of online learning platforms. In this paper, a personalized learning recommendation model based on improved collaborative filtering is proposed. Firstly, a multilayer interest model of learners is established to accurately describe learners’ interest in knowledge topics, courses, and knowledge areas; then, in view of the sparse scoring matrix and cold-start problems of traditional collaborative filtering recommendation algorithms, an improved collaborative filtering-based personality is proposed. The personalized learning recommendation model is used to improve the similarity calculation of users by introducing user initialization tags and solve the cold-start problem of new users. Finally, the effectiveness of the algorithm is proved by experimental comparison, and the improved algorithm improves the recommendation effect of personalized learning.
e audio signal processing flow.
Interaction process of the humming audio score recognition system.
Humming recognition neural network hyperparameters.
  • Nan LiuNan Liu
As one of the hotspots in music information extraction research, music recognition has received extensive attention from scholars in recent years. Most of the current research methods are based on traditional signal processing methods, and there is still a lot of room for improvement in recognition accuracy and recognition efficiency. There are few research studies on music recognition based on deep neural networks. This paper expounds on the basic principles of deep learning and the basic structure and training methods of neural networks. For two kinds of commonly used deep networks, convolutional neural network and recurrent neural network, their typical structures, training methods, advantages, and disadvantages are analyzed. At the same time, a variety of platforms and tools for training deep neural networks are introduced, and their advantages and disadvantages are compared. TensorFlow and Keras frameworks are selected from them, and the practice related to neural network research is carried out. Training lays the foundation. Results show that through the development and experimental demonstration of the prototype system, as well as the comparison with other researchers in the field of humming recognition, it is proved that the deep-learning method can be applied to the humming recognition problem, which can effectively improve the accuracy of humming recognition and improve the recognition time. A convolutional recurrent neural network is designed and implemented, combining the local feature extraction of the convolutional layer and the ability of the recurrent layer to summarize the sequence features, to learn the features of the humming signal, so as to obtain audio features with a higher degree of abstraction and complexity and improve the performance of the humming signal. The ability of neural networks to learn the features of audio signals lays the foundation for an efficient and accurate humming recognition process.
  • Mingxiao LiuMingxiao Liu
  • Joohyun SuhJoohyun Suh
At present, people’s life pressure is increasing. In order to help alleviate the pressure of professional mothers’ upbringing, in this study, the treatment program for working mothers or parental education program was analyzed. A questionnaire survey was conducted among 278 mothers of male and female infants aged 0–5 in three kindergartens in Liaocheng City, Shandong Province. SPSS 23.0 software was used for frequency analysis, correlation analysis, and regression analysis. This study aims to improve the mood and self-esteem of working mothers with young children, and briefly introduce the policy of providing important support in addition to relatives. The results show that: first, the age of children is the cause of mother’s parenting pressure. The younger the children are, the greater the parenting pressure is; second, the higher the mother’s sense of parenting efficacy, the lower the parenting pressure; third, the higher the mother’s self-esteem, the lower the parenting pressure; fourth, the social support that working mothers receive may reduce their childcare pressure. Reducing the parental rearing pressure of working mothers with low self-esteem can help actively promote the positive development of their children. This article provides some reference for the relief of the pressure of professional mothers’ parenting.
  • Xinping WuXinping Wu
  • Xinzhou GengXinzhou Geng
  • Zhiyi ChenZhiyi Chen
  • [...]
  • Jinchao LiJinchao Li
With the deepening of digital transformation and upgrading of power grid enterprises, the digital system evaluation method of power grid enterprises based on experts’ subjective experience has been unable to meet the management needs of modern enterprises. In this paper, a method based on fuzzy information axiom for dynamic design quality evaluation of digital system in electric power enterprises is proposed. Firstly, the electric power enterprise digital system dynamic design quality comprehensive evaluation index system is set up from three aspects, which are achievement degree of target business function, logical relation rationality, and technical economy of physical model. Secondly, the quantitative and qualitative index values are processed by using the information calculation formula of minimum information axiom and fuzzy membership function. And then best-worst method and antientropy weight method are used to form the comprehensive evaluation model. Finally, the feasibility and effectiveness of the design scheme are verified by an example of dynamic design of digital system in power enterprise.
  • Zhenming YuZhenming Yu
In the process of continuous improvement in the quality of vocal music teaching, new opportunities and challenges are ushered in. Teachers need to actively develop innovative and creative teaching activities in line with the teaching objectives and tasks of the new curriculum reform, enhance students’ application and mastery of knowledge points, and provide a boost to students’ high-quality practical learning activities. This paper proposes a method for recommending vocal teaching brands based on review mining and multicriteria decision-making. Firstly, a large number of student reviews are crawled from various university platforms in China, matching student views to lexical combinations of course opinions. The weight of each opinion indicator was calculated by using hierarchical analysis in multicriteria decision-making, and 35 teachers and students were asked to do paired comparison and scoring for the indicators. The results of the experiment suggest improvements to the recommended vocal teaching brands, with practical implications for the design and improvement of vocal music teaching.
Change curve of average precision rate during 50 concept searches on 5000 images.
  • Guanyan GuoGuanyan Guo
  • Liangliang SunLiangliang Sun
Content-based image retrieval (CBIR) is an important part of pattern recognition and artificial intelligence. It has broad application prospects in many important fields, such as digital library, medical image analysis, petroleum geological survey, and public security information retrieval. In this study, statistical modeling and discriminant learning methods are used to analyze and study some key problems in image retrieval, including image concept retrieval, image example retrieval, and relevance feedback. The main research results obtained are as follows: an image classification method based on the Gaussian mixture model (GMM) and max-min posterior pseudo-probability (MMP) discriminant learning is proposed, which is called GMM-MMP method for short; a concept retrieval method based on GMM-MMP is proposed. According to the image concept, the image is divided into two categories: the concept-related image and the concept-unrelated image. The Gaussian mixture model is used to establish the mapping from the image low-level features to the image concept, and the image is classified according to the posterior pseudo-probability classifier to realize the image concept retrieval; an example retrieval method based on GMM-MMP is proposed. According to the image similarity semantics, the image is divided into two categories: the related image and the uncorrelated image of the example image. The Gaussian mixture model is used to establish the mapping from the low-level features of the image to the image similarity semantics. The image is classified according to the posterior pseudo-probability classifier to realize the image case retrieval. Based on the above work, this study implements a content-based image retrieval system.
  • Kuiwu WangKuiwu Wang
  • Qin ZhangQin Zhang
  • Xiaolong HuXiaolong Hu
Distributed multitarget tracking (MTT) is suitable for sensors with limited field of view (FoV). Generalized covariance intersection (GCI) fusion is used to solve the MTT problem based on label probability hypothesis density (PHD) filtering in this paper. Because the traditional GCI fusion only has good fusion performance for the targets in the intersection of each sensor’s FoV, and the targets outside the intersection range would be lost, this paper redivides the Gaussian components according to the FoV and distinguishes the Gaussian components of the targets inside and outside the intersection. GCI fusion is sensitive to label inconsistency between different sensors. For label fusion in the intersection region, the best match of labels is found by minimizing label inconsistency index, and then GCI fusion is performed. Finally, the feasibility and effectiveness of the proposed fusion method are verified by simulation, and its robustness is proved. The proposed method is obviously superior to local sensor and traditional GCI algorithm.
  • Chunliang ZhouChunliang Zhou
  • Weipeng ZhangWeipeng Zhang
  • Zhengqiu LuZhengqiu Lu
  • [...]
  • Ying XuYing Xu
In order to depict the influence of Weibo user, an evaluation model is proposed with Gaussian Bayesian derivatives. At first, the influence indexes of Weibo user is presented in this model with activity degree, relation degree, and coverage degree. Combining the relationship characteristics between users and behavioral characteristics of user, the solved method for this model is given by Gaussian Bayesian derivatives. At last, a simulation is conducted to study the influence factor with experiment data of Sina Weibo users. The results show that, compared to other algorithm, this method has good adaptability.
Schematic diagram of teaching design program of psychological wellness education course.
e framework of psychological wellness education based on DDL.
Mental health-driven decision-making model.
Variance comparison of different mental health factors.
Comparison of the mean square of mental health factors between classes.
  • Ying FangYing Fang
  • Na LiNa Li
  • Sang-Bing TsaiSang-Bing Tsai
Chinese contemporary college students, this generation has been pampered since childhood, growing up under the wings of their parents, most of them are flowers in the hothouse, now stepping into the university and carrying the double expectations of society and family. With the rapid development of modern technology and social culture, people in modern society are facing fierce competition, frequent stress, fast pace, and unprecedented psychological pressure, which has a significant impact on human health. Therefore, the construction of a university psychological wellness education model has become the focus of theoretical research. As a new type of mental health teaching and learning model, data-driven learning (DDL) not only provides learners with rich, diverse, and realistic mental health data, but also creates an ideal learning environment for learners due to its corpus-based teaching and learning characteristics. This paper explores the design of DDL-based mental health teaching and learning for students, combining theoretical research, and empirical analysis from the actual university, and constructing a comprehensive system of psychological wellness education in college while building a local system of DDL-based psychological wellness education. The experimental results show that the Q-learning algorithm and SA-Q algorithm have no environmental triggering mechanism, while the data-driven control algorithm has the step number of formula 24, 19, and 17, respectively, thus reaching the optimal path. Therefore, in the data-driven psychology classroom, students change from participating in activities according to the course teaching objectives and mental health teachers’ requirements to inquiry-based learning and interactive experiences that focuses on problem solving and task completion.
System composition.
Relationship between canopy coverage and RVI, NDVI.
  • Zehua FanZehua Fan
  • Nannan ZhangNannan Zhang
  • Desheng WangDesheng Wang
  • [...]
  • Xuedong ZhangXuedong Zhang
In order to realize cotton growth monitoring, a cotton planting monitoring system based on image processing technology was proposed. The system requires camera to collect cotton canopy images, leaf areometer to detect the leaf area index, and spectrometer to detect the normalized vegetation index ( NDVI ) and the vegetation index ( RVI ). Due to different types of data, based on the establishment of the output transmission system, the canopy coverage was calculated by the image processing method, and there was a linear relationship between canopy coverage NDVI and RVI . The leaf area index (LAI) of N content was established, and the model of dry matter accumulation and canopy coverage was exponential. The experiment result shows that the linear and exponential coefficients of the model k and b increased with the increase of the nitrogen application rate. The fitting determination coefficient remained high under different nitrogen application rates. The fitting coefficient R2 of the three models in the two test fields ranged from 0.83 to 0.923, which also met the needs of model evaluation. The system was used to detect the cotton field with good accuracy.
Cloud GIS overall platform architecture: the function and design scheme of each floor.
Data mining model.
Comparative analysis of satisfaction.
  • Yugang DongYugang Dong
  • Haozhi SuiHaozhi Sui
  • Lei ZhuLei Zhu
In order to ensure that the steel structure of the building can meet the requirements of the strength of the building structure and the strict requirements in the fields of earthquake resistance, fire protection, energy saving, and environmental protection in the construction process, a method based on virtual reality supply chain cloud computing collaborative management technology is proposed. This method analyzes the main technology of building steel structure and establishes personalized cloud computing collaborative management technology based on virtual reality supply chain. Based on the analysis of industry characteristics and personalized service characteristics, the virtual reality control mechanism of personalized data mining is proposed by considering the service time cost and user experience quality. Based on cloud computing and VIRTUAL reality GIS, a collaborative management and control system architecture is proposed to optimize user personalized needs and solve the impact of differentiation on the supply chain. Experimental results show that compared with the noncollaborative management scheme, the collaborative management algorithm proposed by this method reduces the interference of personalized differences on supply chain management by 25%. The experimental results show that this method can greatly guarantee the satisfaction of steel structure construction process.
Schematic diagram of the recurrent neural network mechanism. (a) Single layer recurrent neural network and (b) recurrent neural network expanded by time nodes.
Schematic diagram of the basic functional unit gate structure of the long-short-term memory model.
Company personnel distribution in 2021.
Distribution of technical grades and educational levels of employees in 2021.
Technical staff turnover from 2018 to 2021.
  • Jie HeJie He
  • Jianhua ZhangJianhua Zhang
The introduction of human capital can be traced back to the ancient Greek period, emphasizing the important role of knowledge and skills in the production and life process. Human capital reward mechanism promotes modern social and economic development and is an important part of social and economic growth. The core management of the enterprise is the management of human capital, and the central work of human capital management is the incentive of human capital. Many enterprises are now facing difficulties in industrial operation, serious brain drain, and lack of core competitiveness in the market. As a result, enterprises cannot adapt to the speed and requirements of today’s social and economic development. One of the important reasons is that the enterprise lacks attention to the value of the human capital incentive system, or the human capital incentive system of the enterprise is unreasonable, which leads to a series of problems such as poor employee enthusiasm and low enterprise performance. How to establish a reasonable and effective incentive mechanism to mobilize the enthusiasm and creativity of employees has become a problem that enterprises must pay attention to. Taking the technical managers and general technicians of a high-tech enterprise as an example, combined with the deep learning method, the article made a detailed analysis of the four major incentive factors of enterprise human capital. It made employees’ satisfaction with corporate cultural incentives reaching 56.1%, which showed that emotional motivation was also one of the key factors for employees to be satisfied with the corporate incentive system.
e ranking results produced by the probabilistic linguistic PROMETHEE I method.
:e positive outranking flow, the negative outranking flow, and the net outranking flow.
:e priority rank produced by probabilistic linguistic PROMETHEE I.
:e relative closeness of each scheme by TOPSIS with PLTSs.
  • Wenshuai WuWenshuai Wu
Talent training quality is an important field within higher education research. Innovating the talent training mode and deepening educational reform programs are both of great significance for enhancing the quality of postgraduate innovation and entrepreneurship education in universities. In this study, Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) I and II methods are extended with the probability linguistic term set (PLTS) to accurately express and quantitatively evaluate the reform scheme of postgraduate innovation and entrepreneurship education talent training mode under the big data environment. First, probabilistic linguistic PROMETHEE I and II methods are presented for quantitatively evaluating the reform scheme of postgraduate innovation and entrepreneurship education talent training, which have the advantages of good effectiveness and feasibility. Second, the PLTS is imported into the evaluation methods and applied to accurately depict qualitative information about the index data of the reform scheme effect by the degree of probability. Third, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) with PLTS is proposed to perform a comparative study and conduct visual analysis to verify the effectiveness of the extended probabilistic linguistic PROMETHEE I and II methods. Fourth, an empirical example illustrates the specific evaluation process, verifies the feasibility of the extended methods, and explains the effectiveness of the results. The research findings indicate that the proposed method to reform scheme evaluation can lead to better decision quality, especially in a complex fuzzy and uncertain decision-making environment.
Infinite Timoshenko beam on a foundation with finite depth under an accelerated moving load.
Velocity effect on the deflection shape of the beam.
Deflection shapes of the Timoshenko beam on various foundations.
Maximum displacement in the upward and downward directions on classical Winkler's foundations of velocity 165 (m/s).
  • Amin GhannadiaslAmin Ghannadiasl
  • Hassan Rezaei DolaghHassan Rezaei Dolagh
Simulation of an infinite Timoshenko beam subjected to accelerated moving load rested on the finite depth is assessed in this study. Then, the dynamic response of the infinite beam is illustrated on the various theoretical models, including the Winkler, Pasternak, and visco-elastic foundations. Furthermore, the effects of various damping, such as foundation damping, beam damping, and hysteretic damping (damping between soil grains), are also studied on the dynamic behavior of the beam. It has been worked out that the type of basement and its depth have a remarkable effect on the dynamic behavior. In addition, the load velocity will also cause a maximum displacement in the beam as the critical velocity approaches. The deflection of the beam on the basements increases when the velocity approaches the critical one, and the maximum displacement of the beam occurs under the load. Finally, it was seen that the presented diagrams for all three types of Winkler’s, Pasternak’s, and visco-elastic foundations follow the usual properties related to the critical velocity.
  • Xinyu WuXinyu Wu
  • Yiyang WuYiyang Wu
  • Qilin YingQilin Ying
In the condition of the electricity market, the benefit is the result of market gaming for hydropower stations with price-making ability. The traditional energy maximization model is not appropriate in this circumstance. To study long-term operation variation in the market, a dynamic programming game model of hydropower stations is proposed to obtain a Nash equilibrium solution in long-term time series. A dynamic programming algorithm iteratively solves the model. The proposed approach is applied to hydropower stations of Longtan, Xiaowan, and Goupitan in a hypothetical pure hydropower market. The results show that the dynamic programming game model clearly outperforms the energy maximization model in terms of hydropower station benefit, and the operation results differences using the game model and optimization model are analyzed. For the studied cascaded reservoirs, the benefit increasing percentages can be 4.1%–8.2% with 0.2%–3.8% energy loss in this hypothetical electricity market, comparing the game model to the energy maximization model.
  • Yurong WangYurong Wang
Under the background of the 2030 sustainable performance goal, in the sustainable performance, strategic orientation has been paid much attention because of its importance and management controllability. Whether the market-oriented strategy of enterprises can affect the legitimacy of enterprises and what kind of complex relationship exists between market orientation, legitimacy of enterprises, and enterprise performance. Therefore, from the perspective of digital theory, this paper makes an empirical test on the above problems by introducing the intermediary variable of enterprise legitimacy, so as to make up for the shortcomings of the existing research. When the time index reaches 50, among the four dimensions of the experiment, the average correlation of digital culture is 1.12, the average correlation of resource integration is 0.58, the average correlation of process optimization is 0.74, and the average correlation of technical capability is 0.64. Among the four dimensions, the proportion of digital culture is the highest, and the CITC value of each measurement item in each dimension is greater than 0.5, so the deletion of each item cannot be increased. If the enterprises under digitalization can meet the social expectations and cognition of all stakeholders in the strategic-oriented sustainable performance system, they will be recognized and accepted by them, thus gaining higher legitimacy.
Optimization of data processing steps of news communication platform.
Evaluation index system of Internet public opinion.
Accuracy of sensitive information processing.
Statistics of sensitive information filtering effect test.
  • Chao JiangChao Jiang
  • ZhiXian YangZhiXian Yang
It is urgent to effectively monitor the public opinion of the news communication platform. The platform designed in this paper takes microblog public opinion as the research goal, uses MongoDB to build a distributed computing platform for sensitive information of news communication platform, establishes a corpus of sensitive event topics, introduces PageRank algorithm to deal with microblog social relations, obtains the characteristics of sensitive information of news communication platform, and carries out information screening, so as to accurately screen and mine the keywords in high impact information. To ensure the practical application effect of sensitive information mining method of news communication platform based on big data analysis. Finally, the experiment proves that the sensitive information mining method of news communication platform based on big data analysis has the advantages of high timeliness and high accuracy, which fully meets the research requirements. This is fully in line with the requirements of the study.
Data mining of consumer behavior.
User classification.
e value scores of various types of customers.
Operation of the algorithm.
  • Di ZhangDi Zhang
  • Minghao HuangMinghao Huang
In order to develop a more efficient and accurate marketing strategy for consumers’ purchase behavior, this paper establishes a user value model by modeling and learning the user historical data of e-commerce enterprises. The improved K-means algorithm is used to cluster the purchase behavior of users, and the customer value matrix is constructed from two dimensions of consumption frequency and average consumption amount. Finally, e-commerce users are classified into four categories by marking points. The test results show that the improved K-means algorithm is stable and efficient, and the analysis of user clustering characteristics is helpful to develop more accurate marketing strategies.
Analysis of common theory and common characteristics.
e twin structure of the Great Wall digital twin in the full life cycle.
Research path for the construction of the Great Wall digital twin.
e average score of each index of the subjective evaluation scale.
User's subjective evaluation scale.
  • Haomei JiaHaomei Jia
  • Jing YanJing Yan
In order to preserve and inherit material cultural heritage, the author proposes a method for digital construction of the Great Wall’s cultural heritage based on digital twin. This method discusses the connotation of the whole life cycle of the Great Wall digital twin, proposes the research path and content of the digital twin construction of the Great Wall cultural heritage, and conducts the application of the Great Wall digital twin to evaluate the application effect. The evaluation results show that users’ overall satisfaction with Great Wall’s digital twin application is relatively high, and the average score of each indicator is above 4, and the functional experience is slightly poor. Conclusion. This method can provide full life cycle visualization services for the digital archiving, application, and decision-making of the Great Wall cultural heritage and provide theoretical and methodological references for the preservation and inheritance of material cultural heritage.
  • Yufan HanYufan Han
In order to improve the level of personalized news recommendation efficiently and accurately, an event network-oriented personalized news recommendation algorithm is proposed. First, the event network is used to analyze, predict users’ interests and preferences, and actively push information content to meet users’ personalized needs, so as to build a personalized news recommendation model. Under the mobile Internet technology, combined with the characteristics of the Internet, through the position and title similarity of sentences in the document and other features, the combined features are formed to calculate the sentence weight. Finally, the sentences are extracted according to the weight ranking to generate the news summary, so as to realize the research on personalized news recommendation algorithm for event network. The experimental results show that the proposed algorithm has high recall and coverage, short time, good recommendation effect, and strong recommendation performance.
  • Qingtuan XieQingtuan Xie
  • Qing HanQing Han
  • Dong ChenDong Chen
As an important means of communication, sports public opinion has been paid more and more attention by the country. This article will focus on the emergency events of sports events as the main object and research and analysis of the communication function and law in the public opinion of sports events and analyzes the characteristics of public opinion communication. This paper holds that the media play the “should be” roles in different periods, such as network public opinion monitoring and early warning, public opinion information processing and interpretation, consensus builder highlighting the mainstream, and introspection of public opinion events, and there is a media role cycle in the secondary public opinion period. Spreading law of public opinion in sports events in China: trigger-conduction-outbreak mechanism. At present, the diversified interactive mode of sports events in China makes the communication environment of public opinion in sports events more and more three-dimensional and multidimensional, and it is also convenient for us to make better use of the current regulation means of public opinion in sports events. Sports network public opinion can easily go to “post-truth.” In order to strengthen the monitoring of sports public opinion and improve the ability to respond to network public opinion, using interdisciplinary thinking, this paper proposes a network public opinion guidance strategy for sports events. The experiment is aimed at the disadvantage that the trend of sports public opinion analysis and tracking is not clear. The optimization method is proposed, and the accuracy is improved by 87.5%.
Time history of S, L, B, and P computers with the attributes mentioned in Section 7, in presence of delay.
Time history of S, L, B, and P computers with the attributes mentioned in Section 7, with τ � 30.50.
Time history of S, L, B, and P computers with the attributes mentioned in Section 7, with τ � 35.650 � τ′.
Time history of S, L, B, and P computers with the attributes mentioned in Section 7, with τ � 40.05 > τ′.
  • Zhigang LiuZhigang Liu
  • V. MadhusudananV. Madhusudanan
  • M. N. SrinivasM. N. Srinivas
  • [...]
  • Tadele Degefa GeletoTadele Degefa Geleto
The massive disruptions caused by malware, such as a virus in computer networks and other aspects of information and communication technology, have generated attention, making it a hot research topic. While antivirus and firewalls can be effective, there is also a need to understand the spread patterns of viral infection using epidemic models to curb its incidences. Many previous research attempts have produced analytical models for computer viruses under various infectiousness situations. As a result, we suggested the SLBS model, which considers infection latency and transient immunity in patched nodes. Under certain conditions, the local stability of all equilibrium points is investigated. By setting the delay parameter, we established the occurrence of a Hopf bifurcation (HB) as it crossed a crucial point by several analyses. We also used the centre manifold theorem and normal form theory to examine the attributes of the HB. While the former was used to study the time delay and direction of Hopf bifurcation, the latter was used to investigate external noise and its intensities. Finally, numerical simulations two dimensional and three-dimensional graphs were used to depict the perturbations of the model, thus bolstering the essentiality of the study.
Metric weights for sports intelligence.
  • Lin LiLin Li
  • Wei ZhangWei Zhang
Competitive performance ability is the on-the-spot performance of basketball players’ comprehensive use of technical and tactical, physical, and psychological abilities. Because basketball players’ competitive performance includes many evaluation contents and influencing factors, it is difficult to comprehensively and objectively use a single qualitative analysis or quantitative statistics for evaluation and measurement. Based on the analytic hierarchy process (AHP) and intelligent fuzzy comprehensive evaluation, this research establishes a hybrid model, which is applied to evaluate the performance of basketball players. Based on the actual needs, this research uses the relevant theories in the discipline of sports training, with abundant empirical data materials, combined with the specific practice of basketball players’ training and competition, starting from the concept of clear sports intelligence, to further determine the indicators of the basketball players’ sports intelligence evaluation system and weights, construct a set of evaluation system about basketball players, and use this as a standard to evaluate and judge the current situation of basketball players’ sports intelligence.
The information ow between BRIC and relevant volatilities constitutes a complex network, which needs comprehensive analysis. We provide a rigorous investigation of information ow among stock markets of BRIC and the US VIX in a frequency-domain paradigm. Henceforward, the variation mode decomposition-based entropy approach is employed for the examination of diverse investment horizons and market conditions. First, we nd that under stressed market conditions (lower quantiles), signi cant negative information ow exists between the BRIC constituents and the BRIC composite index. Also, under benign market conditions, we reveal similar dynamics as found at the lower quantiles, which enhances diversification. However, during market booms, we document more positive information ow between the assets and relevant to the redeployment of portfolios. Second, at low probability events representing market stress, we document potential negative information ow amid the stock markets and the US VIX for most investment horizons. Notwithstanding, the US VIX has the potential of transmitting positive information to the stock markets. However, at high market performance, we nd more positive information ow amid the BRIC markets and VIX, generally implying long-term efficiency. Investors, portfolio managers, risk managers, and policy-makers should be wary of the heterogeneous and adaptive behaviour of BRIC stock markets with the VIX.
In this paper, we studied unsteady MHD nanofluid squeezing flow between two parallel plates considering the effect of Joule heating and thermal radiation. The governing equations in the form of partial differential equations (PDEs) are transformed into a system of ordinary differential equations (ODEs) with the help of similarity transformation. The obtained boundary value problem is solved analytically by optimal auxiliary function method (OAFM) and numerically by Runge–Kutta method of order 4 (RKMO4). The OAFM results are validated and compared to the results of RKMO4. The effects of physical parameters such as stretching parameter S , Prandtl number Pr , Eckert number Ec, magnetic number M , volume friction φ electric parameter E 1 , and porous parameter γ on the velocity, temperature, and concentration profiles are discussed with the help of plots. Also, the skin friction and Nusselt numbers effects are discussed with the help of tabular data. As the plates move apart, the Nusselt number and the skin friction coefficient decline and the Prandtl number decreases the temperature profile, whereas the stretching and Eckert number increases causing to increase the temperature field.
The state space method of gear train analysis and the unified mathematical model of state space based gear train analysis are proposed in this paper. Firstly, the concept of basic gear train unit is defined, and the state transformation equation of basic gear train unit is established, which is applied to express the transformation rule of the basic gear train unit and the adjacency relationship between the units. Then, the gear train state space is formed based on the dual vectors including input state eigenvector and output state eigenvector, which describe the state characteristics of the gear train unit. The state space is established based on the mapping relationship among the input-output dual vectors, the state transformation matrix, and the gear train unit. Then the dual vector operation laws and mathematical operation methods in gear train state space are generated. Therefrom, a unified mathematical model based on the kinematic, statics, and structural characteristics of the gear train in the state space is established. Finally, the digital identification and automatic analysis of any complex gear train is realized. The fast comparison and analysis of a large number of gear train schemes is achievable in the scheme design stage.
Under uncertain conditions, the problem of selecting investment timing for telecom network optimization and expansion projects is common in the investment practice of telecom operators. By examining the characteristics of the demand and price of the telecommunications industry, combined with historical data of the telecommunications industry, an exponential function relationship of prices meeting demand is proposed, and then an investment timing decision model under the condition of uncertain demand is established on this basis. Studies have shown that when the value of the investment project is 1.5634 times the investment cost, the value of the investment option is the largest, and this is the optimal investment opportunity. The value of the investment opportunity F(V), the value of the project V ∗ , the degree of demand uncertainty σ , and discount rate ρ are positively correlated, and it is negatively correlated with the opportunity cost δ of delaying project investment and keeping investment options alive.
As a nonuniform structure, tailings dam undergoes complex and chaotic nonlinear changes, under the joint influence of multiple dynamic or nondynamic factors. These changes make it difficult to predict the deformation of tailings dam accurately with a numerical model. To solve the problem, this paper proposes a grey deformation prediction model optimized by double coefficient (GDPM-DC). Unlike a single grey prediction model, the GDPM-DC does not mutate significantly but adapts well to specific scenarios. Besides, the model can smoothen and stabilize the original data and thus achieve accurate prediction of the deformation of tailings dam. The main results are as follows. The GDPM-DC made more accurate predictions than the traditional grey model (1, 1) (GM (1, 1)), the grey model based on logarithmic transformation (GM-LT), and the grey model optimized by weight coefficient (GM-WC). It significantly improved the overall prediction accuracy of the vertical and transverse deformations of the dam and controlled the relative error of the predicted seepage pressure to 2.79%–3.71%. Moreover, the model could forecast the trend component and random fluctuation component of seepage pressure effectively, fit the increasing trend in stages 1–3 and the decreasing trend in stages 3–9, and realize the quantitative prediction of deformation law for the operating tailings dam. The research results provide a meaningful reference for the instability analysis and safety management of tailings dam.
e SEM of the sample (SEM).
e TEM of the sample (TEM).
Electron diffraction pattern of the sample.
e EDS profiles of the sample.
Elemental analysis of the sample.
Ag@AgCl/GO was prepared by chemical coupling, in-situ deposition of supported AgCl, and photoreduction. The morphology, structure, and surface area of the prepared Ag@AgCl/GO were characterized by SEM, TEM, FT-IR, Raman spectra, and BET. The optical properties of the photocatalyst were analyzed by PL and UV-Vis DRS, respectively. The surface electrical properties and degradation stability were evaluated by zeta potential measurement and cyclic catalytic degradation experiments, and the photocatalytic mechanism was proposed in detail based on the ESR test and trapping experiment. The results showed that Ag@AgCl nanoparticles were spherical and cluster distributed on the folded structure of GO. The prepared Ag@AgCl/GO had good adsorption performance and photocatalytic degradation stability. The material showed good visible light catalytic performance; especially, the degradation rates of cationic dyes RhB and MB were significantly higher than those of anionic dyes MO and CR, and their degradation processes were in line with the quasi-first-order reaction kinetics. Holes (h+) and superoxide radicals (·O2-) were the main active species for the degradation of RhB.
The green environment is the need of the hour. There are many techniques opted by the agriculture experts for Hedera nepalensis cutting propagation. In order to simulate the propagation pattern of Hedera nepalensis cuttings more vividly, the simulation method of Hedera nepalensis cuttings propagation based on fractal theory has been studied in this paper for promoting green environment. The rule of L-system to generate to Hedera nepalensis graphics is elaborated in detail. The composition of generators is represented by strings, and Hedera epalensis fractal images are generated repeatedly by recursive algorithm. Combining BSP technology (Binary Space Partitioning Tree) with L-system, the simulation model of Hedera nepalensis cutting propagation is designed. The mechanism and morphological difference of real Hedera nepalensis cutting propagation are investigated in detail. The different Hedera nepalensis organs are distinguished by static simulation and the mathematical model is established. With the help of GDI+, random functions are flexibly used in programming control to simulate the process of Hedera nepalensis cutting propagation. By determining the growth proportion coefficient of each organ and the growth coefficient of control parameters, the image of Hedera nepalensis cutting reproduction is generated continuously from the dynamic control parameters and the dynamic process of Hedera nepalensis cutting reproduction is simulated. The simulation results show that the simulation error of this method is less than 0.8%. The simulation process is simple and efficient, and the simulation results are vivid and viable to suggest newer solution for Hedera nepalensis cutting propagation to protect the environment.
Detailed comparison between HFPRs and FPRs.
Hesitancy and uncertainty features of experts are common in the decision-making process, especially for the project management events. To solve this problem, a novel similarity-based decision-making approach is put forward, as well as an application to the hydraulic engineering project management. Several experts, who are invited in the decision-making process, are suggested to adopt hesitant fuzzy preference relations (HFPRs) to show their evaluations. To measure the similarity degree of experts, a novel integrated similarity index (SI) is given combining the alternative ranking-based similarity index (SIAR) and distance-based similarity index (SID) between HFPRs. The SIAR can be derived from the comparison of the alternative rankings, while the SID depends on evaluations’ distance degree. After that, on the basic of opinion transition probabilities, experts’ weights are allocated, which is necessary for the aggregation process. Then, the collective preferences can be aggregated from the individuals’ evaluations. Afterwards, the above methods along with a score function are adopted to obtain the optimal solution for an actual hydraulic engineering project management event. Finally, for verifying the feasible and effective features of the presented methods, some significative discussions and comparative analyses are provided.
Model of the vehicle's dynamics.
Specification of the vehicle.
:e average value (Case 2).
A vehicle’s oscillation can be improved by using an active suspension system to replace the conventional passive suspension system. The active suspension system operates on the control signal from the controller. This paper introduces the Fuzzy-PI integrated control algorithm to control the system’s efficiency. The Fuzzy algorithm adjusts the KP and KI parameters of the controller. This response depends on the oscillation state of the vehicle. A quarter model with a hydraulic actuator was used in this research. The simulation process takes place in the MATLAB-Simulink environment. In the first case, the maximum and average values of displacement and acceleration of the sprung mass when the vehicle uses the Fuzzy-PI algorithm are only 2.68%, 1.34%, and 1.65%, 0.66%, respectively, compared to the situation of vehicles using the passive suspension system. For the second case, these values are 3.33%, 1.69%, and 2.01%, 0.73%, respectively. As a result, the vehicle’s stability and comfort are greatly enhanced when the integrated controller is used. Besides, the change of KP and KI parameters is also completely suitable for the vehicle’s driving conditions. This research only leads to calculations and simulations; the experimental process is expected to occur soon.
Evaluation index system.
Data investigation and analysis table of 10 decision units in an area.
In this paper, the data envelopment analysis (DEA) approach is put forward to deal with the spatial efficiency evaluation issue for the outdoor environment in high-rise residential (HRR) areas in China. The principles of quantifiable and objectivity are utilized to empirically research for the area of 10 high-rise residential houses. For the outdoor environment in HRR areas, our aim is to take appropriate optimization suggestions that not only satisfy the different conditions of the outdoor environment but also improve the efficiency of site space. By resorting to the data envelopment analysis technique, some practical methods are acquired for evaluating the outdoor environment for HRR areas.
Contents of impact indicators of urban marathon events.
Consumption-marketing model comparison chart.
The arrival of the national fitness boom has enabled the marathon as a large-scale sports event to attract the attention of a sufficient number of sports enthusiasts in a relatively short period of time. These people attracted by the event will have a series of expenses and expenditures also precisely reflect the prominent role of sports events on social and economic effects. In this paper, the AISAS consumption behavior model is used as the theoretical research model, and the survey data of urban marathon participants is used as the basis for analysis. The methods of generating attention, interest surveys, information search methods, actual consumption items and amounts during the action, and postmatch satisfaction and information sharing willingness and methods have been analyzed and counted in detail, and spss21.0 software is used to analyze the statistics. The results are analyzed. In addition, this paper studies the effect of the subspace weighting algorithm based on the singular value of random matrix on the urban marathon. Using the results of the eigenvalues of the sample covariance matrix in random matrix theory, the energy of each subspace is estimated, and then the subspace weighting matrix is constructed with the estimated energy. Through the calculation of the weights of the indicators, the weights of the three-level indicators of the event quality are ranked in the order of participation experience, cooperative media level, enthusiasm of local residents to watch the competition, registration status of the competition, number of sponsors, quality of sponsors, enthusiasm of local residents to participate in the competition, media value, and number of cooperative media algorithm, in which the marathon economic effect is defined as the estimated angle where the deviation between the estimated value and the true value exceeds 20%, that is, the root mean square error of the estimated angle is greater than 10%. In the first-level indicators, the quality of the event to evaluate the impact of the event itself has been added. The quality of the event is the influence of the urban marathon itself, which originates from the quality of the marathon itself. At the same time, the quality of the urban marathon is also the foundation of its economic, social, and environmental impact on the host site, so the quality of the urban marathon is an important impact indicator.
Labeled pictures.
Output-II results.
Facial dataset of people with/without wearing a mask.
Comparison of accuracy achieved through different approaches.
Coronavirus disease 2019 (COVID-19) has a significant impact on human life. The novel pandemic forced humans to change their lifestyles. Scientists have broken through the vaccine in many countries, but the face mask is the only protection for public interaction. In this study, deep neural networks (DNN) have been employed to determine the persons wearing masks correctly. The faster region-based convolutional neural networks (RCNN) model has been used to train the data using graphics processing unit (GPU) device. To achieve our goals, we used a multiphase detection model: first, to label the face mask, and second to detect the edge and compute edge projection for the chosen face region within the face mask. The current findings revealed that faster RCNN was efficient and precise, giving 97% accuracy. The overall loss after 200,000 epochs is 0.0503, with a trend to decrease. While the loss is falling, we are getting more accurate results. As a result, the faster RCNN technique effectively identifies whether a person is wearing face masks or not, and the training period was decreased with better accuracy. In the future, Deep Neural Network (DNN) might be used first to train the data and then compress the dimensions of the input to run it on low-powered devices, resulting in a lower computational cost. Our proposed system can achieve high face detection accuracy and coarsely obtain face posture estimation based on the specified rule. The faster RCNN learning algorithm returns high precision, and the model’s lower computational cost is achieved on GPU. We use the “label-image” application to label the photographs extracted from the dataset and apply Inception V2 of faster RCNN for face mask detection and classification.
Balanced search process in genetic algorithm.
e specific steps of the genetic algorithm.
Comparative analysis of the application effects of different communication strategies.
With the advent of the era of global economic integration, more and more cities are involved in the increasingly fierce competition in the form of image dissemination in order to obtain better economic benefits. The choice of media for this city to carry out image communication has become an inevitable choice for the survival and development of the city. However, since the effect of the current urban media image dissemination is not too ideal, in order to promote the dissemination and development of urban media image, this study made use of global discretization, which is widely used, to study the urban media image dissemination strategy. The research results prove that it is very important to combine global discretization and urban media image for development. Because, the combination of the two can improve the dissemination efficiency and effectiveness of the city’s media image to the greatest extent, enhance the city’s global popularity and competitiveness, and drive a large flow of capital and high-quality talents to the city. In order to obtain greater economic benefits in the wave of global economic integration and to stand out in the increasingly competitive urban environment, a firm foothold is gained in the global economy.
The Semantic Accessibility Scale (SAS) is one of the criteria for systematically assessing the semantic readability of corpus texts. With the advent of the Internet, English language content has been widely distributed. This constitutes an adequate corpus for corpus research. However, how to assign and evaluate the semantic acceptability of English literary texts with the aid of corpus has become a hot topic of study for academics around the world. In this paper, we propose an analysis method for corpus semantic acceptance based on the Kano model. This method combines the Kano model with corpus semantic acceptance. Initially, the method identifies the initial corpus semantic acceptance demand items using the initial corpus semantic acceptance identification questionnaire. The Kano categories of each requirement item are then identified and filtered based on the second Kano questionnaire. In this paper, we propose a Kano-based method for corpus semantic acceptance requirement analysis and apply Kano theory to corpus semantic acceptance requirement analysis. First, the corpus semantic acceptance requirements are classified into corresponding Kano categories and filtered using a Kano survey; second, the initial weights of the corpus semantic acceptance requirements are determined using the coarse number method; and finally, the initial weights of the requirements are adjusted using the corresponding Kano adjustment coefficients to obtain the final weights of the corpus semantic acceptance requirements.
Framework of the EPC mode.
Hexi Corridor region, Gansu Province.
Differences between traditional project contracting mode and EPC mode in PTTPs.
Correspondence between linguistic sets and TFNs.
Power transmission and transformation projects (PTTPs) under new energy grid connections are different from ordinary engineering construction projects. With large investment amounts, various supporting facilities, and high safety and quality requirements, PTTPs are vulnerable to the volatility of new energy power generation, climate, geological conditions, geopolitical environment, technological changes, and other kinds of uncertain factors. Therefore, the investment risk of PTTPs under new energy grid connection is particularly considerable in the project management. For the owner of PTTPs, adopting the engineering-procurement-construction (EPC) mode is an effective attempt to solve the project construction problems faced by the owners. Thus, this paper deeply excavates the key risk points of PTTPs in the initial investment phase under the EPC mode and constructs the novel risk evaluation index system from the perspectives of economy, management, policy, society, and environment. An assessment model is established based on triangle fuzzy number-hesitant fuzzy linguistic term sets (HFLTS-TFNs), the entropy method, and the fuzzy comprehensive evaluation method, which is used to evaluate the investment risk of PTTPs to provide reference for power grid corporations to control project risks. Finally, a case study in the Hexi Corridor region, Gansu Province, China, is illustrated to demonstrate the rationality of the decision model and find management risks are the most vital risk factors for PTTPs under EPC mode.
In the Internet of Things (IoT) ecosystem, localization is critical for tracking and monitoring targets via nodes. The distance vector-hop (DV-Hop) technique is a good choice for localizing neighborhood in IoT networks. The conventional DV-Hop algorithm is a distributed localization approach that does not consider the distribution of the nodes into full deliberation when calculating the hop count from the source to destined nodes. The transfer distance and node positions thus do not attain higher efficiency while ascertaining the distance between sources and destined nodes. The study aims to resolve the pitfalls in the traditional algorithm by making enhancements in controlling the original DV-Hop algorithm’s hop count and transfer distance method by utilizing the particle swarm to estimate the node positions. Error rate in the distance between beacon nodes and unseen nodes is effectively reduced with the proposed technique that calculate error factors with corrections in a reversed fashion to revise hop counts. An escape factor is introduced to take control of updating particles’ velocity in the system, and the inertia weight is defined by a piecewise function to enlarge search space. This mechanism increases the diversity of the particle populations and mitigates the tendency of estimations on node positions to be trapped into local optima under stationary state. Also, the improved DV-Hop algorithm described in the paper has a better convergence speed due to the presence of random inertia weight logarithmic method. Finally, the problem of premature convergence is also tackled as a variation factor is adopted in collaboration with a fitness function that affects the particles’ movement range and assists in global convergence. The overall performance of improved DV-Hop is evaluated by statistical metrics and also compared with the traditional DV-Hop algorithm under simulated environment with the data collected from real-world scenarios. Industry 4.0 is fully dependent upon IoT and the count of hops is very important for deciding the routing from the source to destination for speedy transmission of data. The improved DV-hop algorithm can achieve better results and has reduced error rate by more than 30%. The DV-Hop algorithm plays an important role in IoT-enabled environment especially in Industry 4.0.
In this paper, the random matrix of multivariate statistical analysis is used to conduct in-depth research and analysis of the university coordination utility management and online repair platform. Considering that the chunking of variables based on mechanistic knowledge is not easy to achieve, firstly, the maximum correlation and minimum redundancy algorithm is used to portray the correlation more accurately between process variables and remove the redundancy between variables to provide the optimal variable input for the base model. The multivariate mean control chart was used to calculate the offset between the data of each test group of the contact network and the overall mean and standard values of the contact network parameters under different correlations among the contact network parameters. Based on the daily work research and process document sampling of the university coordination utilities management department, the requirement analysis and design of the target system were completed, and a university coordination utility management system based on BS architecture was developed. Student information is lost, data statistics are wrong, etc., so that the business work of other departments of the school cannot be carried out smoothly. The whole platform can be divided into several submodules according to the functions: super administrator module, administrator module, staff module, and user module, and the detailed design scheme of each module is described in detail. At the same time, the logistic regression model is trained using the collected data sets, and the training scheme of the model is designed. The mathematical model of logistic regression and the related algorithm are used to decide whether to purchase maintenance equipment at this stage and the quantity of purchase. Finally, a new monitoring index is proposed to monitor the process status. MNPE-GMM not only maintains most of the local structural information of the window dataset in the feature subspace but also reduces the computational complexity of GMM in the fault detection process. The MNPE-GMM method can effectively improve the fault detection rate of multimodal intermittent processes by introducing new statistics.
This paper presents an in-depth study and analysis of the reliability of indicators for the evaluation of sports training using the algorithm of random matrices. Random matrix theory is used to obtain the degree of randomness of the data and the overall correlation characteristics of the data through statistical analysis of the energy spectrum and eigenstates of complex systems. Using the results of random matrix theory on the eigenvalues of the sample covariance matrix, the energy of each subspace is estimated, and the estimated energy is then used to construct the subspace weighting matrix. The problem of reliable finite-time stability of the model under several types of uncertain time-lagged bounded transfer probability Markov jump parameter compatibility indicators is also explored. The four basic theoretical issues, namely, value orientation, construction basis, construction principles, and conceptual model of the sports training work evaluation index system, are studied. This paper takes the construction of a sports training evaluation index system as the research object and uses the literature method, expert interview method, questionnaire survey method, data analysis method, Delphi method, and hierarchical analysis method as the main research methods to construct a set of sports training evaluation index system as the research task, to enrich the idea of sports training evaluation work, and to provide a reference for the future government to be able to develop a sports training evaluation index system that is in line with the development of the times. The research method was used to collect the opinions of experts. The subjective opinions given by experts were collected using the assignment method, and the weight coefficients of each index were calculated using the AHP hierarchical analysis method. The result is that the index of basic conditions (0.3591) has the largest weight in the whole system. After that, organization and management (0.1911), work effectiveness (0.1376), staffing (0.103), activity development (0.0828), financial and fund management (0.0562), publicity and reporting (0.0382), and material provision (0.032) are in order. The evaluation results of the index system are consistent with the current actual situation of specialization teaching, indicating that the index system for evaluating the effectiveness of high school sports training has good validity.
e improvements in computation facility and technology support the development and implementation of automatic methods for medical data assessment. is study tries to extend a framework for e ciently classifying chest radiographs (X-rays) into normal/COVID-19 class. e proposed framework consists subsequent phases: (i) image resizing, (ii) deep features extraction using a pretrained deep learning method (PDLM), (iii) handcrafted feature extraction, (iv) feature optimization with Brownian May y-Algorithm (BMA), (v) serial integration of optimized features, and (vi) binary classi cation with 10-fold cross validation. In addition, this work implements two methodologies: (i) performance evaluation of the existing PDLM in the literature and (ii) improving the COVID-19 detection performance of chosen PDLM with this proposal. e experimental investigation of this study authenticates that the e ort performed using pretrained VGG16 with SoftMax helped get a classi cation accuracy of >94%. Further, the research performed using the proposed framework with BMA selected features (VGG16 + handcrafted features) helps achieve a classi cation accuracy of 99.17% on the chosen X-ray image database. is outcome proves the scienti c importance of the implemented framework, and in the future, this proposal can be adopted to inspect the clinically collected X-rays.
DW model is improved based on the unique characters of group consumption. At first, using social experiment, we determine the categories and attributes of agents, and then based on the DW model, participants’ interaction rules are established. Finally, a mass of numerical simulation experiments show that in the form of collaborative online shopping, merchants can reverse opinions, who could persuade consumers with negative attitudes, changing opinions, and supporting collaborative online shopping, and the level of characteristics is closely related to the number of consumers changing opinions; opinion leaders can differentiate group opinions, neither accelerating effect nor destructive effect; the characteristics level of individual consumers has close relation with positive group polarization effect; when an individual consumer has high conformity or trust propensity, the opinions of some customers with supporting collaborative online shopping will be strengthened, they accept collaborative online shopping more. In addition, for group interaction of collaborative online shopping context, there is no negative group polarization effect.
Educational data mining.
In recent years, educational data mining has gained a considerable lot of interest as a consequence of the large number of pedagogical content that can be gathered from a range of sources. This is because there is a lot of instructional information that can be obtained. The data mining tools collaborate with academics to improve students’ learning strategies by analyzing, sifting through, and estimating components that are pertinent to students’ characteristics or patterns of behavior. This is accomplished through the following steps: EDM is utilized in the vast majority of instances to develop the classification model, which then assigns a certain class to each student based on the known properties of the training dataset. Before putting the classification model into use, it is possible to utilize a test dataset to verify that the model is accurate. This article provides a description of a recommendation system that determines the most beneficial academic program for students by utilizing fuzzy logic and machine learning. The compilation of a student dataset has begun. It includes a total of 21 features and 1000 individual cases. The initial step is to employ the CFS attribute selection method. This methodology selects 15 of the initial set of 21 characteristics. Following the completion of the data gathering, it is put through various machine learning methods such fuzzy SVM, random forest, and C4.5. This methodology that has been offered makes predictions about the academic program that is best suitable for students.
Let D = V , E be an oriented graph with minimum out-degree δ + . For x ∈ V D , let d D + x and d D + + x be the out-degree and second out-degree of x in D , respectively. For a directed graph D , we say that a vertex x ∈ V D is a Seymour vertex if d D + + x ≥ d D + x . Seymour in 1990 conjectured that each oriented graph has a Seymour vertex. A directed graph D is called m -free if there are no directed cycles with length at most m in D . A directed graph D = V , E is called k -transitive if, for any directed x y -path of length k , there exists x , y ∈ E . In this paper, we show that (1) each δ + − 2 -free oriented graph has a Seymour vertex and (2) each vertex with minimum out-degree in m -free and 2 m + 2 -transitive oriented graph is a Seymour vertex. The latter result improves a theorem of Daamouch (2021).
Diseases have been studied separately, but two diseases have inherent dependencies on each other, modelling them separately negates practical reality. The authors’ modelling processes are based on univariate separate regressions, which connect each illness to covariates separately. Therefore, the focus of this article is to estimate the spatial correlation within geographic regions using latent variables. Individual and areal-level information, as well as spatially dependent random effects for each spatial unit, are incorporated into the models developed using a hierarchical structure. Simulation techniques provide to assess the models’ performance using Bayesian computing approaches (INLA and MCMC). The findings show a reasonable performance of the DIC and RMSE values of the proposed latent model. From that, the model can be considered as the best compared to the shared component model, multivariate conditional autoregressive model, and univariate models.
Evaluation of the carrying capacity of mineral resources is one of the important research content in the implementation of sustainable development. Based on analyzing the metallogenic geological characteristics, distribution, and resource status of mineral resources in southern Shaanxi, this paper establishes an analysis model of mineral resources and mineral advantages based on the analytic hierarchy process and applies them to evaluate the advantages of mineral resources. To provide optimal and efficient results, an improved model of an artificial neural network based on the bat optimization algorithm has been utilized. Through model analysis, the potential value and carrying capacity of mineral resources in three major prefecture-level cities in southern Shaanxi are comprehensively evaluated and analyzed. The results show that the main dominant minerals in southern Shaanxi are gold, lead zinc, and molybdenum ore. There are three grades of mineral resources carrying capacity: Shangluo City is an excellent grade, Hanzhong City is a good grade, and Ankang City is a general grade.
Journal metrics
9 days
Submission to first decision
60 days
Submission to final decision
22 days
Acceptance to publication
Acceptance rate
1.430 (2021)
Journal Impact Factor™
2.1 (2021)
Top-cited authors
José A. Tenreiro Machado
  • Polytechnic Institute of Porto
Yu-Dong Zhang
  • University of Leicester
Shuihua Wang
  • University of Leicester
Genlin ji
  • Nanjing Normal University
Ming Li
  • Zhejiang University/East China Normal University