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The development of social economy and the continuous advancement of science and technology have opened the historical prelude of the information age. Streaming media technology, with its wide audience, diverse forms, and strong guiding technology, has infinitely narrowed the time and space distances of people in different regions of the world. The difficulty of political work has very important practical significance. Therefore, this paper focuses on the theme of the reform of the teaching method system of the ideological and political theory course in colleges and universities and the analysis of the education system. From the perspective of streaming media technology, we learn the opportunities and challenges faced by college students in the transformation of ideological education and the reform of education system and explain the strategies and measures for the transformation of ideological education and the reform of education system in college students. We hope that this study can provide some theoretical support for the ideological education and teaching of college students. The problem of data scheduling for ideological and political education in the P2P system is analyzed, the data scheduling problem is formalized, and the form of optimal data scheduling is analyzed. Through theoretical analysis, the optimal scheduling problem is transformed into an equivalent minimum cost flow problem that can be calculated in binomial time. It is proved by reasoning that the consistency of the optimal data scheduling problem and the minimum cost flow problem is verified, and the correctness and feasibility of the viewpoint are verified.
Lian Xu; Sang-Bing Tsai. The Transformation of College Students’ Ideological and Political Education and Learning Analysis of Education System by Streaming Media Technology. Mathematical Problems in Engineering 2021, 2021, 1 -11.
AMA StyleLian Xu, Sang-Bing Tsai. The Transformation of College Students’ Ideological and Political Education and Learning Analysis of Education System by Streaming Media Technology. Mathematical Problems in Engineering. 2021; 2021 ():1-11.
Chicago/Turabian StyleLian Xu; Sang-Bing Tsai. 2021. "The Transformation of College Students’ Ideological and Political Education and Learning Analysis of Education System by Streaming Media Technology." Mathematical Problems in Engineering 2021, no. : 1-11.
With the advent of the information age, the way people obtain information has changed profoundly. The wave of informationization in higher education has also come with it, and the teaching mode, teaching content, and teaching form are constantly innovated. How to organically integrate information technology into education teaching in order to care for learners’ learning experience and promote the cultivation of new talents is an issue that current educational technology researchers need to pay great attention to. This paper first builds a complete blended teaching model of public English for higher education, but its application effect needs to be further examined. This paper is an investigation in the background of the current era to build a blended teaching model. Based on the continuous development of the era, the ideology and application technology of this field will keep upgrading, so the teaching model also needs to be changed and updated according to the characteristics of the development of the era. The investigation of mixed teaching modes is not permanent. The investigation of the mixed teaching mode is not permanent. At present, only a few courses apply the blended teaching mode. On the basis of the continuous updating of teaching concepts and the latest technologies, it is foreseen that the focus of subsequent investigations will be on the individualized development of the blended teaching mode.
Qianqian Xie; Sang-Bing Tsai. An Empirical Study on Innovation of College Blended Teaching under Big Data Analysis. Mathematical Problems in Engineering 2021, 2021, 1 -9.
AMA StyleQianqian Xie, Sang-Bing Tsai. An Empirical Study on Innovation of College Blended Teaching under Big Data Analysis. Mathematical Problems in Engineering. 2021; 2021 ():1-9.
Chicago/Turabian StyleQianqian Xie; Sang-Bing Tsai. 2021. "An Empirical Study on Innovation of College Blended Teaching under Big Data Analysis." Mathematical Problems in Engineering 2021, no. : 1-9.
This paper proposes the functional model and application service implementation process of the education cloud platform application service architecture. The entire cloud application service architecture mainly includes four parts: cloud service management, cloud application service rapid creation and deployment, dynamic process configuration, and unified identity authentication. Based on the basic theory of workflow, the process status and business services of cloud application services are discussed. The BP neural network weight optimization model based on the improved quantum evolution method is studied, and a method that combines the improved quantum evolution algorithm (IQEA) and the BP algorithm to complete the back propagation neural network training is proposed, that is, the IQEA-BP algorithm. Firstly, the traditional quantum evolution algorithm is improved, and then, the improved quantum evolution algorithm is used to optimize the network weights as a whole to overcome the shortcomings of the BP algorithm that is easy to fall into the local optimum; then, we use the BP algorithm to find the better weight as the initial value to improve the training and prediction accuracy of the network. In order to enrich the school education quality evaluation system, this article adds soft indicators that can reflect school education performance on the basis of the existing “National Education Inspection Team” indicators and uses analytical methods to prove the effectiveness and feasibility of the new evaluation indicators. The X1-X10 index data is selected as the evaluation index of the school education quality evaluation system in this paper. Testing the performance of the BP neural network, the accuracy rate of the school education quality evaluation is 93.3%, the average absolute error is 0.067, and the accuracy and recall rate of the test set grade gradient of 0, 1, 2, 3, 5, 6, and 8 are all 93%, indicating that the IQEA-BP neural network algorithm has a good effect on the evaluation of school education quality.
Hong-Xia Liu; Yong-Heng Zhang; Sang-Bing Tsai. Cloud Education Chain and Education Quality Evaluation Based on Hybrid Quantum Neural Network Algorithm. Wireless Communications and Mobile Computing 2021, 2021, 1 -11.
AMA StyleHong-Xia Liu, Yong-Heng Zhang, Sang-Bing Tsai. Cloud Education Chain and Education Quality Evaluation Based on Hybrid Quantum Neural Network Algorithm. Wireless Communications and Mobile Computing. 2021; 2021 ():1-11.
Chicago/Turabian StyleHong-Xia Liu; Yong-Heng Zhang; Sang-Bing Tsai. 2021. "Cloud Education Chain and Education Quality Evaluation Based on Hybrid Quantum Neural Network Algorithm." Wireless Communications and Mobile Computing 2021, no. : 1-11.
The vision of a Smart City involves enriching the quality of life by gaining insights from data collected from interconnected sensors, devices, and people. Perpetual urban issues such as security, waste management, transportation, and traffic can be addressed by utilizing data to improve efficiency; however, to do this, all data needs to be stored in a location in which it can be easily accessed and used by all stakeholders, both private and governmental. The cloud service will help break down intergovernmental silos wherein different departments have no clear channel to communicate and understand data-based priorities of other departments—a factor seen as a major impediment to Smart City adoption. Security is also a major aspect of the new product, as the continued perpetuation of the “Internet of Things” has (and will) created demonstrable security concerns.
Sang-Bing Tsai; B. B. Gupta; Dharma P. Agrawal; Wenqing Wu; Aijun Liu. Recent Advances in Intelligent Transportation Systems for Cloud-Enabled Smart Cities. Journal of Advanced Transportation 2021, 2021, 1 -2.
AMA StyleSang-Bing Tsai, B. B. Gupta, Dharma P. Agrawal, Wenqing Wu, Aijun Liu. Recent Advances in Intelligent Transportation Systems for Cloud-Enabled Smart Cities. Journal of Advanced Transportation. 2021; 2021 ():1-2.
Chicago/Turabian StyleSang-Bing Tsai; B. B. Gupta; Dharma P. Agrawal; Wenqing Wu; Aijun Liu. 2021. "Recent Advances in Intelligent Transportation Systems for Cloud-Enabled Smart Cities." Journal of Advanced Transportation 2021, no. : 1-2.
With the continuous development of neural network theory itself and related theories and related technologies, neural network is one of the main branches of intelligent control technology. Artificial neural network is a nonlinear and adaptive information processing composed of a large number of processing units. In this paper, an adaptive fuzzy neural network (FNN) is used to construct an intelligent system architecture for English learning, and activation function is used to apply the knowledge of computer science and linguistics to English learning. The network neural structure diagram is presented. English machine learning model framework is established based on recursive neural network. On this basis, feature vector extraction and normalization algorithm are used to meet the needs of neural network model. After acquiring the feature vectors of users’ learning styles, the clustering algorithm is used to effectively form a variety of learning styles. The validity of the English learning model was verified by designing the functional flow based on tests. Accurate mastery can activate the corresponding brain regions not only to improve the efficiency of learning, but also to better facilitate language learning.
He Dong; Sang-Bing Tsai. An Empirical Study on Application of Machine Learning and Neural Network in English Learning. Mathematical Problems in Engineering 2021, 2021, 1 -9.
AMA StyleHe Dong, Sang-Bing Tsai. An Empirical Study on Application of Machine Learning and Neural Network in English Learning. Mathematical Problems in Engineering. 2021; 2021 ():1-9.
Chicago/Turabian StyleHe Dong; Sang-Bing Tsai. 2021. "An Empirical Study on Application of Machine Learning and Neural Network in English Learning." Mathematical Problems in Engineering 2021, no. : 1-9.
We study a firm's strategy in adopting big data technology to motivate consumer demand over two periods. In the first period, the firm designs a product to sell to the market and determines whether to apply big data to attract more consumers. In the second period, the firm designs a new product and determines whether to sell the old product and the new product simultaneously, where big data can also be applied in this period to stimulate more demands. We formulate this problem into four models considering whether the firm adopts big data in the first period and/or the second period, and whether the firm only sells the new product or sells both the old and new products in the second period. We find that the firm prefers to apply big data over both periods when the cost is low, only over the second period when the cost is median and will not apply big data when the cost is high. Interestingly, only applying big data over the first period also may bring the most profits with heterogeneous big data coefficient. Furthermore, applying big data in the second period is the better choice for the social welfare.
Lei Yang; Anqiang Jiang; Jiahua Zhang. Optimal timing of big data application in a two-period decision model with new product sales. Computers & Industrial Engineering 2021, 160, 107550 .
AMA StyleLei Yang, Anqiang Jiang, Jiahua Zhang. Optimal timing of big data application in a two-period decision model with new product sales. Computers & Industrial Engineering. 2021; 160 ():107550.
Chicago/Turabian StyleLei Yang; Anqiang Jiang; Jiahua Zhang. 2021. "Optimal timing of big data application in a two-period decision model with new product sales." Computers & Industrial Engineering 160, no. : 107550.
Due to the lack of macro and systematic data, the target cost of high-star hotel project cannot meet the characteristics and needs of the hotel project itself. Therefore, the establishment of star hotel development scale prediction is urgent. In the scale development strategy, based on the previous studies, combined with the development characteristics of regional high-star hotels in a city, this paper constructs the index system of influencing factors of the development scale of high-star hotels and extracts the main influencing factors of hotel development scale by principal component analysis and partial relationship analysis, which are mainly urban development, economic development, tourism development, tourism development exhibition industry development, business development, and transportation development. The BP artificial neural network prediction method is used to establish a prediction model for the development scale of high-star hotels, by adopting the above key extraction factors as input of BP neural network. Through the input and output of the scale influence index data, the development scale of star hotels is accurately predicted. The simulation results verify the effectiveness and reliability of the star hotel development scale prediction strategy based on BP neural network, in terms of accuracy and model superiority.
Nan Zhao; Sang-Bing Tsai. Research on Prediction Model of Hotels’ Development Scale Based on BP Artificial Neural Network Algorithm. Mathematical Problems in Engineering 2021, 2021, 1 -12.
AMA StyleNan Zhao, Sang-Bing Tsai. Research on Prediction Model of Hotels’ Development Scale Based on BP Artificial Neural Network Algorithm. Mathematical Problems in Engineering. 2021; 2021 ():1-12.
Chicago/Turabian StyleNan Zhao; Sang-Bing Tsai. 2021. "Research on Prediction Model of Hotels’ Development Scale Based on BP Artificial Neural Network Algorithm." Mathematical Problems in Engineering 2021, no. : 1-12.
In order to improve the accuracy of sports combination training action recognition, a sports combination training action recognition model based on SMO algorithm optimization model and artificial intelligence is proposed. In this paper, by expanding the standard action data, the standard database of score comparison is established, and the system architecture and the key acquisition module design based on 3D data are given. In this paper, the background subtraction method is used to process the sports video image to obtain the sports action contour and realize the sports action segmentation and feature extraction, and the artificial intelligence neural network is used to train the feature vector to establish the sports action recognition classifier. This paper mainly uses a three-stream CNN artificial intelligence deep learning framework based on convolutional neural network and uses a soft Vlad representation algorithm based on data decoding to learn the action features. Through the data enhancement of the existing action database, it uses support vector machine to achieve high-precision action classification. The test results show that the model improves the recognition rate of sports action and reduces the error recognition rate, which can meet the online recognition requirements of sports action.
Hecai Jiang; Sang-Bing Tsai. An Empirical Study on Sports Combination Training Action Recognition Based on SMO Algorithm Optimization Model and Artificial Intelligence. Mathematical Problems in Engineering 2021, 2021, 1 -11.
AMA StyleHecai Jiang, Sang-Bing Tsai. An Empirical Study on Sports Combination Training Action Recognition Based on SMO Algorithm Optimization Model and Artificial Intelligence. Mathematical Problems in Engineering. 2021; 2021 ():1-11.
Chicago/Turabian StyleHecai Jiang; Sang-Bing Tsai. 2021. "An Empirical Study on Sports Combination Training Action Recognition Based on SMO Algorithm Optimization Model and Artificial Intelligence." Mathematical Problems in Engineering 2021, no. : 1-11.
In this paper, we conduct in-depth research and analysis on the intelligent recognition and teaching of English fuzzy text through parallel projection and region expansion. Multisense Soft Cluster Vector (MSCVec), a multisense word vector model based on nonnegative matrix decomposition and sparse soft clustering, is constructed. The MSCVec model is a monolingual word vector model, which uses nonnegative matrix decomposition of positive point mutual information between words and contexts to extract low-rank expressions of mixed semantics of multisense words and then uses sparse. It uses the nonnegative matrix decomposition of the positive pointwise mutual information between words and contexts to extract the low-rank expressions of the mixed semantics of the polysemous words and then uses the sparse soft clustering algorithm to partition the multiple word senses of the polysemous words and also obtains the global sense of the polysemous word affiliation distribution; the specific polysemous word cluster classes are determined based on the negative mean log-likelihood of the global affiliation between the contextual semantics and the polysemous words, and finally, the polysemous word vectors are learned using the Fast text model under the extended dictionary word set. The advantage of the MSCVec model is that it is an unsupervised learning process without any knowledge base, and the substring representation in the model ensures the generation of unregistered word vectors; in addition, the global affiliation of the MSCVec model can also expect polysemantic word vectors to single word vectors. Compared with the traditional static word vectors, MSCVec shows excellent results in both word similarity and downstream text classification task experiments. The two sets of features are then fused and extended into new semantic features, and similarity classification experiments and stack generalization experiments are designed for comparison. In the cross-lingual sentence-level similarity detection task, SCLVec cross-lingual word vector lexical-level features outperform MSCVec multisense word vector features as the input embedding layer; deep semantic sentence-level features trained by twin recurrent neural networks outperform the semantic features of twin convolutional neural networks; extensions of traditional statistical features can effectively improve cross-lingual similarity detection performance, especially cross-lingual topic model (BL-LDA); the stack generalization integration approach maximizes the error rate of the underlying classifier and improves the detection accuracy.
Ling Liu; Sang-Bing Tsai. Intelligent Recognition and Teaching of English Fuzzy Texts Based on Fuzzy Computing and Big Data. Wireless Communications and Mobile Computing 2021, 2021, 1 -10.
AMA StyleLing Liu, Sang-Bing Tsai. Intelligent Recognition and Teaching of English Fuzzy Texts Based on Fuzzy Computing and Big Data. Wireless Communications and Mobile Computing. 2021; 2021 ():1-10.
Chicago/Turabian StyleLing Liu; Sang-Bing Tsai. 2021. "Intelligent Recognition and Teaching of English Fuzzy Texts Based on Fuzzy Computing and Big Data." Wireless Communications and Mobile Computing 2021, no. : 1-10.
The ID3 algorithm is a key and important method in existing data mining, and its rules are simple and easy to understand and have high application value. If the decision tree algorithm is applied to the online data migration of sports competition actions, it can grasp the sports competition rules in the relationship between massive data to guide sports competition. This paper analyzes the application performance of the traditional ID3 algorithm in online data migration of sports competition actions; realizes the application steps and data processing process of the traditional ID3 algorithm, including original data collection, original data preprocessing, data preparation, constructing a decision tree, data mining, and making a comprehensive evaluation of the traditional ID3 algorithm; and clarifies the problems of the traditional ID3 algorithm. Mainly, the problems of missing attributes and overfitting are clarified, which provide directions for the subsequent algorithm optimization. Then, this paper proposes a k -nearest neighbor-based ID3 optimization algorithm, which selects values similar to k -nearest neighbors to fill in the missing values for the attribute missing problem of the traditional ID3 algorithm. Based on this, the improved algorithm is applied to the online data migration of sports competition actions, and the application effect is evaluated. The results show that the performance of the k -nearest neighbor-based ID3 optimization algorithm is significantly improved, and it can also solve the overfitting problem existing in the traditional ID3 algorithm. For the overall classification problem of six types of samples of travel patterns, the experimental data samples have the characteristics of high data quality, a considerable number of samples, and obvious sample differentiation. Therefore, this paper also uses the deep factorization machine algorithm based on deep learning to classify the six classes of travel patterns of sports competition action data using the previously extracted relevant features. The research in this paper provides a more accurate method and a higher-performance online data migration model for sports competition action data mining.
Li Ju; Lei Huang; Sang-Bing Tsai. Online Data Migration Model and ID3 Algorithm in Sports Competition Action Data Mining Application. Wireless Communications and Mobile Computing 2021, 2021, 1 -11.
AMA StyleLi Ju, Lei Huang, Sang-Bing Tsai. Online Data Migration Model and ID3 Algorithm in Sports Competition Action Data Mining Application. Wireless Communications and Mobile Computing. 2021; 2021 ():1-11.
Chicago/Turabian StyleLi Ju; Lei Huang; Sang-Bing Tsai. 2021. "Online Data Migration Model and ID3 Algorithm in Sports Competition Action Data Mining Application." Wireless Communications and Mobile Computing 2021, no. : 1-11.
The development of economic forestry industry is an important support in the process of rural revitalization strategy and precise poverty alleviation, as well as enrichment of the people. As the market of economic forestry products is close to a perfectly competitive market, brand effect is crucial under homogeneous competition, and economic forestry product enterprises and other business entities need to win and maintain sustainable competitive advantages through brand management. Currently, in the field of economic forest, products such as Chinese wolfberry, jujube, blueberry, fungus, walnut, tephrosia, hazelnut, and chestnut and forest foods and product brands with certain market recognition have emerged, but for most small- and medium-sized economic forest product enterprises, forest product brand cultivation and construction are still in the initial stage. Under the rapid development of the Internet, different types of Internet platforms, which provide new tools and possibilities for branding, the way of corporate brand marketing, and customer management services, have also undergone significant changes. In the market with serious homogenization and increasingly fierce competition, how to establish brand-consumer connection through the Internet platform, strengthen the intensity of consumer participation and connection to the brand, give play to the brand effect, and enhance the brand value in the long term, so as to obtain a new way to win a sustainable competitive advantage, has become an important proposition for all kinds of enterprises, including economic forest product enterprises. Combining the competitive characteristics of economic forestry products and the development of the Internet, the model of brand value enhancement of economic forestry products based on virtual brand communities is constructed. The model takes the experience value obtained by consumers in the virtual brand community as the antecedent and studies the path relationship from experience value, community identity, brand fit, and consumer brand value creation to brand value in four dimensions, utilitarian experience value, emotional experience value, social experience value, and learning experience value, and takes community integration and community support feeling as the moderating variables.
Qingru Duan; Lianbao Kan; Sang-Bing Tsai. Analysis on Forestry Economic Growth Index Based on Internet Big Data. Mathematical Problems in Engineering 2021, 2021, 1 -11.
AMA StyleQingru Duan, Lianbao Kan, Sang-Bing Tsai. Analysis on Forestry Economic Growth Index Based on Internet Big Data. Mathematical Problems in Engineering. 2021; 2021 ():1-11.
Chicago/Turabian StyleQingru Duan; Lianbao Kan; Sang-Bing Tsai. 2021. "Analysis on Forestry Economic Growth Index Based on Internet Big Data." Mathematical Problems in Engineering 2021, no. : 1-11.
The comprehensive B2C online marketing is analyzed, and the current situation and shortage of comprehensive B2C online marketing strategies are summarized. Then, based on the relevant theories of consumer behavior and online marketing, the model of influencing factors in the purchasing decision-making process of online consumers is preliminarily constructed, the online purchasing behavior of consumers is studied by means of questionnaire survey, and the model is revised and improved through data collection and verification. Finally, based on the model, the online marketing strategy is discussed from the aspects of comprehensive B2C online marketing construction, product positioning, price strategy, channel construction, website design, and so on. It has important guiding significance to comprehensive B2C online marketing practice. Aiming at the B2C online marketing problem of multimodel fusion with multiobservation samples, a new multimodel fusion B2C online marketing algorithm based on LS-SVM is proposed, which is suitable for multiobservation samples. In each B2C online marketing of multimodel fusion, the mode of B2C online marketing to be multimodel fusion is represented by the multiobservation sample set. Firstly, the label of the multiobservation sample set is assumed, and this assumption condition is taken as the constraint condition of the optimization problem in LS-SVM. Thus, the B2C online marketing error of multimodel fusion is obtained. The category of multiobservation samples was determined by comparing the B2C online marketing errors of multimodel fusion under two assumptions. The B2C network marketing prediction method, stacking integrated learning method based on multimodel fusion, is adopted to build a multimachine learning algorithm embedded into the stacking integrated learning B2C network marketing prediction model. Through verification, it shows that the lower the correlation degree, the better the model prediction effect. Compared with the traditional single-model prediction, the B2C network marketing prediction method based on multimodel fusion stacking integrated learning method has higher prediction accuracy. The model prediction effect is better.
Huiru Liao; Sang-Bing Tsai. Research on the B2C Online Marketing Effect Based on the LS-SVM Algorithm and Multimodel Fusion. Mathematical Problems in Engineering 2021, 2021, 1 -11.
AMA StyleHuiru Liao, Sang-Bing Tsai. Research on the B2C Online Marketing Effect Based on the LS-SVM Algorithm and Multimodel Fusion. Mathematical Problems in Engineering. 2021; 2021 ():1-11.
Chicago/Turabian StyleHuiru Liao; Sang-Bing Tsai. 2021. "Research on the B2C Online Marketing Effect Based on the LS-SVM Algorithm and Multimodel Fusion." Mathematical Problems in Engineering 2021, no. : 1-11.
Regional cultural and creative products are paying more attention to the cultural core of their design and communication, while satisfying the basic design elements of appearance, function, and aesthetics; therefore, agricultural cultural and creative image (ACCI) also has its dual attributes of culture and commerce. Virtual reality (VR) animation technology can integrate video, text, and model into one, comprehensively display the three-dimensional digital content of regional cultural characteristics, bring users an immersive viewing experience, and enhance their interest in traditional culture, beneficial to the spreading and inheriting traditional culture. On the basis of summarizing and analyzing previous research works, this paper analyzed the development background, current status, and future challenges of VR animation technology and expounded the research situation and significance of design and dissemination of ACCI. Further, this paper proposed the design method, communication model, and approaches of ACCI based on VR animation technology, explored the reshaping of ACCI’s digital elements, resolution of ACCI’s artistic features, and discovery of ACCI’s artistic values, constructed the platform architecture and implementation technology of VR animation, and finally discussed the issue of the integration and innovation of cultural products and VR animation technology. Excellent regional cultural and creative products can achieve the multiple goals of promoting product sales, improving design aesthetics, and spreading cultural characteristics by pursuing cultural and creative values. The ACCI based on VR animation technology not only makes customers pay attention to the agricultural brands and consuming their products but also promotes regional identity and disseminate regional culture, inspiring the potential awareness of tourism and shopping and driving the economic development.
Qinggang Sun; Sang-Bing Tsai. An Empirical Study on Application of Virtual Reality Animation Technology by Big Data Model. Mathematical Problems in Engineering 2021, 2021, 1 -9.
AMA StyleQinggang Sun, Sang-Bing Tsai. An Empirical Study on Application of Virtual Reality Animation Technology by Big Data Model. Mathematical Problems in Engineering. 2021; 2021 ():1-9.
Chicago/Turabian StyleQinggang Sun; Sang-Bing Tsai. 2021. "An Empirical Study on Application of Virtual Reality Animation Technology by Big Data Model." Mathematical Problems in Engineering 2021, no. : 1-9.
The reverse logistics of municipal hazardous waste (RLMHW) have received close attention from researchers and practitioners alike, given the essential impact of safe transportation and effective management of hazardous waste on public health and environmental sustainability. There are a great number of studies in the extant literature on RLMHW, with many and diverse research topics; however, a concise and complete overview of the research works already conducted in this particular area is conspicuous by its absence. This paper strives to fill the gap through the conduct of rigorous systematic literature review of RLMHW in the past three decades, and then establish a framework of studies on RLMHW. The main contributions of this study are as follows: (1) to identify the trend of journals publishing research papers on RLMHW; (2) to extract the main topics in studies on RLMHW; (3) to locate the most popular research areas of RLMHW; (4) to summarize the methods adopted in studies on RLMHW; (5) to identify research deficiencies in certain categories of RLMHW; and (6) to establish the future research directions of RLMHW. The main implications of the study are to offer a better understanding of RLMHW by systematic crystallization of archival data in a systematic chronological order across central issues. This study contributes to scholarly debate in this field by serving as a snapshot paper to document the development of the field and gives input to policymakers in process design and policy making in the domain of RLMHW.
Chunlin Xin; Jie Wang; Ziping Wang; Chia-Huei Wu; Muhammad Nawaz; Sang-Bing Tsai. Reverse logistics research of municipal hazardous waste: a literature review. Environment, Development and Sustainability 2021, 1 -37.
AMA StyleChunlin Xin, Jie Wang, Ziping Wang, Chia-Huei Wu, Muhammad Nawaz, Sang-Bing Tsai. Reverse logistics research of municipal hazardous waste: a literature review. Environment, Development and Sustainability. 2021; ():1-37.
Chicago/Turabian StyleChunlin Xin; Jie Wang; Ziping Wang; Chia-Huei Wu; Muhammad Nawaz; Sang-Bing Tsai. 2021. "Reverse logistics research of municipal hazardous waste: a literature review." Environment, Development and Sustainability , no. : 1-37.
In this paper, we conduct an in-depth study and analysis of “Internet+ Business English” teaching through teaching big data model and visualization and elaborate on the change of educational objectives in the context of Internet education. From the perspective of individual value coordinates, the goal of education is to adhere to the people-oriented, personalized, and comprehensive free development of human beings; from the perspective of social value coordinates, the goal of education is to cultivate innovative talents for social innovation and development; through the multiple perspectives of the curriculum in the context of Internet education, we analyze the goal orientation and value reshaping of the curriculum in the context of educational change. The curriculum of the future will develop toward an intelligent curriculum and introduces the curriculum design and the form of curriculum organization in the context of Internet education. A comparison of the constructed regression and classification of a total of 27 data models reveals that the model constructed based on all data in the integrated education system is the most effective. The multiple linear regression model explained up to 66.5% of the variance in student academic performance; the explanatory power of the social and demographic characteristics dimension variables ranged from approximately 13% to 18%, the personal characteristics dimension variables ranged from 7% to 20%, and the student input dimension variables ranged from 10% to 17%. The highest correct prediction rate of the binary logistic regression model was 69%.
Hua Zhang; Sang-Bing Tsai. An Empirical Study on Big Data Model and Visualization of Internet+ Teaching. Mathematical Problems in Engineering 2021, 2021, 1 -10.
AMA StyleHua Zhang, Sang-Bing Tsai. An Empirical Study on Big Data Model and Visualization of Internet+ Teaching. Mathematical Problems in Engineering. 2021; 2021 ():1-10.
Chicago/Turabian StyleHua Zhang; Sang-Bing Tsai. 2021. "An Empirical Study on Big Data Model and Visualization of Internet+ Teaching." Mathematical Problems in Engineering 2021, no. : 1-10.
In recent years, with the development of machine learning and big data technology, user data has become an important element in the production process of enterprises. For today’s e-commerce platforms, the deep mining of user’s purchase behavior is helpful to understand user’s purchase preferences and accurately recommend products that meet user expectations, which can not only improve user satisfaction but also reduce platform marketing cost. To accurately identify the user value of online purchasing on an e-commerce platform, this paper uses an improved RFM model to extract user features and uses the K -means++ clustering algorithm to realize user classification. The indicators of the traditional RFM model characterize user features from three angles: recent purchase time ( R ), purchase frequency ( F ), and total consumption amount ( M ). The user group and scenarios studied in this paper are different from the previous literature: (1) the user group is relatively fixed, (2) the consumer goods are relatively single, and (3) the characteristics of repeated purchase are obvious. Therefore, based on the existing literature, this paper extracts the user characteristics studied and improves and models the traditional indicators. Based on the real purchasing data from September to December 2018, it calculates the indicators that improved RFM, empowers the weight to indicators, and finally classifies the value of users by using the K -means++ algorithm. The experimental results show that the user classification based on the improved RFM model is more accurate than the user classification based on the traditional RFM model, and the improved RFM model can identify the user value more accurately, which provides a strong support for the e-commerce platform to realize the accurate marketing strategy based on big data.
Jun Wu; Li Shi; Liping Yang; Xiaxia Niu; Yuanyuan Li; Xiaodong Cui; Sang-Bing Tsai; Yunbo Zhang. User Value Identification Based on Improved RFM Model and K -Means++ Algorithm for Complex Data Analysis. Wireless Communications and Mobile Computing 2021, 2021, 1 -8.
AMA StyleJun Wu, Li Shi, Liping Yang, Xiaxia Niu, Yuanyuan Li, Xiaodong Cui, Sang-Bing Tsai, Yunbo Zhang. User Value Identification Based on Improved RFM Model and K -Means++ Algorithm for Complex Data Analysis. Wireless Communications and Mobile Computing. 2021; 2021 ():1-8.
Chicago/Turabian StyleJun Wu; Li Shi; Liping Yang; Xiaxia Niu; Yuanyuan Li; Xiaodong Cui; Sang-Bing Tsai; Yunbo Zhang. 2021. "User Value Identification Based on Improved RFM Model and K -Means++ Algorithm for Complex Data Analysis." Wireless Communications and Mobile Computing 2021, no. : 1-8.
In this paper, a decision-making system for precision marketing is presented to deal with real-world problems based on real e-business data collected in a company in Beijing. During the data preprocessing, the authors conducted a cleaning course to make sure the data to be analyzed in the latter part of the paper were credential. Based on the processed data, the authors analyzed consumer purchasing behaviors using three classic recommendation algorithms and made a performance comparison of the three algorithms. At the end of this paper, the authors proposed a series of precision marketing strategies which had been adopted by the data source company and had been proved to be effective in improving the performance.
Jun Wu; Li Shi; Guangshu Xu; Yu-Hsi Yuan; Sang-Bing Tsai; Weiyi Hao; Jiesong Jiang. Using the Mathematical Model on Precision Marketing with Online Transaction Data Computing. Mathematical Problems in Engineering 2021, 2021, 1 -7.
AMA StyleJun Wu, Li Shi, Guangshu Xu, Yu-Hsi Yuan, Sang-Bing Tsai, Weiyi Hao, Jiesong Jiang. Using the Mathematical Model on Precision Marketing with Online Transaction Data Computing. Mathematical Problems in Engineering. 2021; 2021 ():1-7.
Chicago/Turabian StyleJun Wu; Li Shi; Guangshu Xu; Yu-Hsi Yuan; Sang-Bing Tsai; Weiyi Hao; Jiesong Jiang. 2021. "Using the Mathematical Model on Precision Marketing with Online Transaction Data Computing." Mathematical Problems in Engineering 2021, no. : 1-7.
Solid waste management and air pollution are two pressing issues in the functioning of large cities. This paper studies the optimization problem of the green transportation route of municipal solid waste and establishes a mathematical planning model based on real-time traffic conditions of the city and consideration of a time window and multiple transfer stations with the goal of minimizing energy consumption. In the optimal green transportation process in this paper, comprehensive consideration of vehicle speed, vehicle load, road gradient, and driving distance in different road sections based on real-time traffic conditions is incorporated, which has a better fuel-saving potential than the shortest path. A green transportation program can alleviate the air pollution problem in big cities and promote energy conservation and emission reduction in solid waste transportation.
Chunlin Xin; Lingjie Wang; Bin Liu; Yu-Hsi Yuan; Sang-Bing Tsai. An Empirical Study for Green Transportation Scheme of Municipal Solid Waste Based on Complex Data Model Analysis. Mathematical Problems in Engineering 2021, 2021, 1 -17.
AMA StyleChunlin Xin, Lingjie Wang, Bin Liu, Yu-Hsi Yuan, Sang-Bing Tsai. An Empirical Study for Green Transportation Scheme of Municipal Solid Waste Based on Complex Data Model Analysis. Mathematical Problems in Engineering. 2021; 2021 ():1-17.
Chicago/Turabian StyleChunlin Xin; Lingjie Wang; Bin Liu; Yu-Hsi Yuan; Sang-Bing Tsai. 2021. "An Empirical Study for Green Transportation Scheme of Municipal Solid Waste Based on Complex Data Model Analysis." Mathematical Problems in Engineering 2021, no. : 1-17.
Effective and efficient closed-loop supply chain processes can provide a significant competitive edge for companies. This study considered three investment strategies in the process of initiating closed-loop supply chain alliances. The results showed that a promised proportion has a significant effect on investment decisions under a pure investment strategy. Furthermore, a reasonable promised proportion can coordinate the supply chain under a pure innovation strategy but cannot in a pure advertising strategy. Upstream (i.e., innovation) investments decrease wholesale and retail prices, while downstream ones increase retail and wholesale prices. Increasing innovation investment can transform benefits to the downstream, while increasing advertising investment may cause opportunism. A hybrid investment strategy balances upstream and downstream investment simultaneously and provides insights into optimizing the supply chain system in investments.
Jiang-Tao Wang; Jian-Jun Yu; Yu-Hsi Yuan; Sang-Bing Tsai; Shu-Fen Zhang. An Empirical Study on Optimal the Allocations in Advertising and Operation Innovation on Supply Chain Alliance for Complex Data Analysis. Wireless Communications and Mobile Computing 2021, 2021, 1 -11.
AMA StyleJiang-Tao Wang, Jian-Jun Yu, Yu-Hsi Yuan, Sang-Bing Tsai, Shu-Fen Zhang. An Empirical Study on Optimal the Allocations in Advertising and Operation Innovation on Supply Chain Alliance for Complex Data Analysis. Wireless Communications and Mobile Computing. 2021; 2021 ():1-11.
Chicago/Turabian StyleJiang-Tao Wang; Jian-Jun Yu; Yu-Hsi Yuan; Sang-Bing Tsai; Shu-Fen Zhang. 2021. "An Empirical Study on Optimal the Allocations in Advertising and Operation Innovation on Supply Chain Alliance for Complex Data Analysis." Wireless Communications and Mobile Computing 2021, no. : 1-11.
Purpose As social networking sites (SNSs) gain popularity, they are being widely used by entrepreneurs to obtain social capital to carry out business ventures. However not all SNS usage behaviors promote entrepreneurship. Only when individuals actively participate in SNSs relationship maintenance behaviors they can obtain resources that are conducive to promoting social entrepreneurship. The aim of this study is to explore the role of WeChat relationship maintenance behavior (WRMB) on social entrepreneurial intention (SEI) with dual narcissism as an essential antecedent that affects SNS use. Design/methodology/approach Based on dual narcissism theory and the theoretical framework that networking is a critical skill and activity for the success of social entrepreneurship, this study proposes a serial mediation model that explores the formation of SEI. This study collected data from a sample of 275 MBA students in China and applied multiple regression and confirmatory factor analysis techniques to test the research model. Findings The results reveal narcissistic admiration (NA) is positively associated with WRMB, while narcissistic rivalry (NR) is negatively associated with such behavior. And the positive impact of NA on SEI can be explained by WRMB and social capital paths, while the negative impact of NR cannot. Originality/value This research is the first application of dual narcissism in the field of SEI, which provides a new way to explain the antecedents of SEI under the social network. The findings provide an effective reference path for social entrepreneurship education in universities and educational institutions and enlighten the correct distinction between dual narcissism in entrepreneurial psychological consultation.
Wenqing Wu; Yuzheng Su; Chia-Huei Wu; Sang-Bing Tsai; Yu-Hsi Yuan. WeChat relationships maintenance behavior and social entrepreneurial intention under conditions of dual narcissism: the mediating role of social capital. Information Technology & People 2021, ahead-of-p, 1 .
AMA StyleWenqing Wu, Yuzheng Su, Chia-Huei Wu, Sang-Bing Tsai, Yu-Hsi Yuan. WeChat relationships maintenance behavior and social entrepreneurial intention under conditions of dual narcissism: the mediating role of social capital. Information Technology & People. 2021; ahead-of-p (ahead-of-p):1.
Chicago/Turabian StyleWenqing Wu; Yuzheng Su; Chia-Huei Wu; Sang-Bing Tsai; Yu-Hsi Yuan. 2021. "WeChat relationships maintenance behavior and social entrepreneurial intention under conditions of dual narcissism: the mediating role of social capital." Information Technology & People ahead-of-p, no. ahead-of-p: 1.