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In wireless sensor networks, sensor nodes are deployed to collect data, perform calculations, and forward information to either other nodes or sink nodes. Recently, geographic routing has become extremely popular because it only requires the locations of sensor nodes and is very efficient. However, the local minimum phenomenon, which hinders greedy forwarding, is a major problem in geographic routing. This phenomenon is attributed to an area called a hole that lacks active sensors, which either prevents the packet from being forwarded to a destination node or produces a long detour path. In order to solve the hole problem, mechanisms to detect holes and determine landmark nodes have been proposed. Following the proposed mechanisms, landmark-based routing was developed in which the source node first sends a packet to the landmark node, and the landmark node then sends the packet to the destination. However, this approach often creates a constant node sequence, causing nodes that perform routing tasks to quickly run out of energy thus producing larger holes. In this paper, a new approach is proposed in which two virtual ellipses are created with the source, landmark, and destination nodes. Forwarding is then guided along virtual ellipses. Furthermore, a recursive algorithm is designed to ensure a shortcut even if there are multiple holes or a hole has multiple landmarks. Thus, the proposed approach improves both geographic routing and energy efficiency routing. Simulation experiments show that the proposed approach increases the battery life of sensor nodes, lowers the end-to-end delay, and generates a short path.
Jianjun Yang. An ellipse-guided routing algorithm in wireless sensor networks. Digital Communications and Networks 2021, 1 .
AMA StyleJianjun Yang. An ellipse-guided routing algorithm in wireless sensor networks. Digital Communications and Networks. 2021; ():1.
Chicago/Turabian StyleJianjun Yang. 2021. "An ellipse-guided routing algorithm in wireless sensor networks." Digital Communications and Networks , no. : 1.
As an important part of human resources, college graduates are the most vigorous, energetic, and creative group in society. The employment of college graduates is not only related to the vital interests of graduates themselves and the general public, but also related to the sustainable and healthy development of higher education and the country’s prosperity through science and education. However, the outbreak of COVID-19 at the end of 2019 has left China’s domestic labor and employment market in severe condition, which has a significant impact on the employment of college graduates. Based on the situation, the Chinese government has formulated a series of employment promotion policies for college graduates in accordance with local conditions to solve the current difficulties in employment of college graduates during the COVID-19Pandemic. Do these policies meet the expectations of the people? Is the policy implementation process reasonable? All these issues need to be tested and clarified urgently. This paper takes the employment promotion policy of college graduates under the COVID-19 as the research object, uses the PMC index model to screen the policy texts, obtains two perfect policy texts, and uses the Weibo comments to construct the evaluation model of policy measures support degree to analyze the social effects of employment promotion policies for college graduates. The results show that the public’s support degree with the employment promotion policies for college graduates under COVID-19 needs to be improved. Among them, the public has a neutral attitude towards position measures and transference measures but is obviously dissatisfied with subsidy measures and channel measures. Finally, suggestions for improving policy are given to make the employment policy in line with public opinion and effectively relieve the job hunting pressure of college graduates.
Tinggui Chen; Jingtao Rong; Lijuan Peng; Jianjun Yang; Guodong Cong; Jing Fang. Analysis of Social Effects on Employment Promotion Policies for College Graduates Based on Data Mining for Online Use Review in China during the COVID-19 Pandemic. Healthcare 2021, 9, 846 .
AMA StyleTinggui Chen, Jingtao Rong, Lijuan Peng, Jianjun Yang, Guodong Cong, Jing Fang. Analysis of Social Effects on Employment Promotion Policies for College Graduates Based on Data Mining for Online Use Review in China during the COVID-19 Pandemic. Healthcare. 2021; 9 (7):846.
Chicago/Turabian StyleTinggui Chen; Jingtao Rong; Lijuan Peng; Jianjun Yang; Guodong Cong; Jing Fang. 2021. "Analysis of Social Effects on Employment Promotion Policies for College Graduates Based on Data Mining for Online Use Review in China during the COVID-19 Pandemic." Healthcare 9, no. 7: 846.
With highly developed social media, English learning Applications have become a new type of mobile learning resources, and online comments posted by users after using them have not only become an important source of intellectual competition for enterprises, but can also help understand customers’ requirements, thereby improving product functionalities and service quality, and solve the pain points of product iteration and innovation. Based on this, this paper crawled the online user comments of three typical APPs (BaiCiZhan, MoMoBeiDanCi and BuBeiDanCi), through emotion analysis and hotspot mining technology, to obtain user requirements and then the K-means clustering method was used to analyze user requirements. Finally, quantile regression is used to find out which user needs have an impact on the downloads of English vocabulary APPs. The results show that: (1) Positive comments have a more significant impact on users’ downloads behavior than negative online comments. (2) English vocabulary APPs with higher downloads, both the 5-star user ratings and the increase of emotional requirement have a negative effect on the increase in APP downloads, while the enterprise’s service requirement improvement has a positive effect on the increase of APP downloads. (3) Regarding English vocabulary APPs with average or high downloads, improving the adaptability and Appearance requirements have significant negative impact on downloads. (4) The functional requirements to improve products will have a significant positive impact on the increase in downloads of English vocabulary APPs.
Tinggui Chen; Lijuan Peng; Jianjun Yang; Guodong Cong. Analysis of User Needs on Downloading Behavior of English Vocabulary APPs Based on Data Mining for Online Comments. Mathematics 2021, 9, 1341 .
AMA StyleTinggui Chen, Lijuan Peng, Jianjun Yang, Guodong Cong. Analysis of User Needs on Downloading Behavior of English Vocabulary APPs Based on Data Mining for Online Comments. Mathematics. 2021; 9 (12):1341.
Chicago/Turabian StyleTinggui Chen; Lijuan Peng; Jianjun Yang; Guodong Cong. 2021. "Analysis of User Needs on Downloading Behavior of English Vocabulary APPs Based on Data Mining for Online Comments." Mathematics 9, no. 12: 1341.
With the rapid development of “We media” technology, netizens can freely express their opinions regarding enterprise products on a network platform. Consequently, online public opinion about enterprises has become a prominent issue. Negative comments posted by some netizens may trigger negative public opinion, which can have a significant impact on an enterprise’s image. From the perspective of helping enterprises deal with negative public opinion, this paper combines user portrait technology and a random forest algorithm to help enterprises identify high-risk users who have posted negative comments and thus may trigger negative public opinion. In this way, enterprises can monitor the public opinion of high-risk users to prevent negative public opinion events. Firstly, we crawled the information of users participating in discussions of product experience, and we constructed a portrait of enterprise public opinion users. Then, the characteristics of the portraits were quantified into indicators such as the user’s activity, the user’s influence, and the user’s emotional tendency, and the indicators were sorted. According to the order of the indicators, the users were divided into high-risk, moderate-risk, and low-risk categories. Next, a supervised high-risk user identification model for this classification was established, based on a random forest algorithm. In turn, the trained random forest identifier can be used to predict whether the authors of newly published public opinion information are high-risk users. Finally, a back propagation neural network algorithm was used to identify users and compared with the results of model recognition in this paper. The results showed that the average recognition accuracy of the back propagation neural network is only 72.33%, while the average recognition accuracy of the model constructed in this paper is as high as 98.49%, which verifies the feasibility and accuracy of the proposed random forest recognition method.
Tinggui Chen; Xiaohua Yin; Lijuan Peng; Jingtao Rong; Jianjun Yang; Guodong Cong. Monitoring and Recognizing Enterprise Public Opinion from High-Risk Users Based on User Portrait and Random Forest Algorithm. Axioms 2021, 10, 106 .
AMA StyleTinggui Chen, Xiaohua Yin, Lijuan Peng, Jingtao Rong, Jianjun Yang, Guodong Cong. Monitoring and Recognizing Enterprise Public Opinion from High-Risk Users Based on User Portrait and Random Forest Algorithm. Axioms. 2021; 10 (2):106.
Chicago/Turabian StyleTinggui Chen; Xiaohua Yin; Lijuan Peng; Jingtao Rong; Jianjun Yang; Guodong Cong. 2021. "Monitoring and Recognizing Enterprise Public Opinion from High-Risk Users Based on User Portrait and Random Forest Algorithm." Axioms 10, no. 2: 106.
The wide dissemination of false information and the frequent occurrence of extreme speeches on online social platforms have become increasingly prominent, which impact on the harmony and stability of society. In order to solve the problems in the dissemination and polarization of public opinion over online social platforms, it is necessary to conduct in-depth research on the formation mechanism of the dissemination and polarization of public opinion. This article appends individual communicating willingness and forgetting effects to the Susceptible-Exposed-Infected-Recovered (SEIR) model to describe individual state transitions; secondly, it introduces three heterogeneous factors describing the characteristics of individual differences in the Jager-Amblard (J-A) model, namely: Individual conformity, individual conservative degree, and inter-individual relationship strength in order to reflect the different roles of individual heterogeneity in the opinions interaction; thirdly, it integrates the improved SEIR model and J-A model to construct the SEIR-JA model to study the formation mechanism of public opinion dissemination and polarization. Transmission parameters and polarization parameters are simulated and analyzed. Finally, a public opinion event from the pricing of China’s self-developed COVID-19 vaccine are used, and related Weibo comment data about this event are also collected so as to verify the rationality and effectiveness of the proposed model.
Tinggui Chen; Jingtao Rong; Jianjun Yang; Guodong Cong; Gongfa Li. Combining Public Opinion Dissemination with Polarization Process Considering Individual Heterogeneity. Healthcare 2021, 9, 176 .
AMA StyleTinggui Chen, Jingtao Rong, Jianjun Yang, Guodong Cong, Gongfa Li. Combining Public Opinion Dissemination with Polarization Process Considering Individual Heterogeneity. Healthcare. 2021; 9 (2):176.
Chicago/Turabian StyleTinggui Chen; Jingtao Rong; Jianjun Yang; Guodong Cong; Gongfa Li. 2021. "Combining Public Opinion Dissemination with Polarization Process Considering Individual Heterogeneity." Healthcare 9, no. 2: 176.
With the development of Internet technology, the speed of information dissemination and accelerated updates result in frequent discussion of topics and expressions of public opinion. In general, multi-dimensional discussion topics related to the same event are often generated in the network, and the phenomenon of multi-dimensional public opinion polarization is formed under the mutual influence of groups. This paper targets the phenomenon of multi-dimensional public opinion polarization under topic-derived situations as the research object. Firstly, this paper identifies the factors influencing multi-dimensional public opinion polarization, including the mutual influence of different topic dimensions and the interaction of viewpoints within the same topic. Secondly, the topic correlation coefficient is introduced to describe the correlation among topics in different dimensions, and the individual topic support degree is used to measure the influence of topics in different dimensions and that of information from external intervention on individual attitudes. Thirdly, a multi-dimensional public opinion polarization model is constructed by further integrating multi-dimensional attitude interaction rules. Finally, the influence of individual participation, topic status, topic correlation coefficient and external intervention information on the multi-dimensional public opinion polarization process is analyzed through simulation experiments. The simulation results show that:(1) when there is a negative correlation between multi-dimensional topics, as the number of participants on different dimensional topics becomes more consistent, the conflict between multi-dimensional topics will weaken the polarization effect of overall public opinion. However, the effect of public opinion polarization will be enhanced alongwith the enhancement in the confidence of individual opinions. (2) The intervention of external intervention information in different dimensions at different times will further form a multi-dimensional and multi-stage public opinion polarization, and when the multi-dimensional topics are negatively correlated, the intervention of external intervention information will have a stronger impact on the multi-dimensional and multi-stage public opinion polarization process. Finally, the rationality and validity of the proposed model are verified by a real case.
Tinggui Chen; Yulong Wang; Jianjun Yang; Guodong Cong. Modeling Multidimensional Public Opinion Polarization Process under the Context of Derived Topics. International Journal of Environmental Research and Public Health 2021, 18, 472 .
AMA StyleTinggui Chen, Yulong Wang, Jianjun Yang, Guodong Cong. Modeling Multidimensional Public Opinion Polarization Process under the Context of Derived Topics. International Journal of Environmental Research and Public Health. 2021; 18 (2):472.
Chicago/Turabian StyleTinggui Chen; Yulong Wang; Jianjun Yang; Guodong Cong. 2021. "Modeling Multidimensional Public Opinion Polarization Process under the Context of Derived Topics." International Journal of Environmental Research and Public Health 18, no. 2: 472.
Background and Aim: At the end of 2019, the outbreak of COVID-19 had a significant impact on China’s tourism industry, which was almost at a standstill in the short-term. After reaching the preliminarily stable state, the government and the scenic area management department implemented a series of incentive policies in order to speed up the recovery of the tourism industry. Therefore, analyzing all sorts of social effects after policy implementation is of guiding significance for the government and the scenic areas. Methods: Targeted as the social effect with the implementation of tourism promotion policy during the COVID-19 pandemic, this paper briefly analyzes the impact of COVID-19 on the national cultural and tourism industry and selects several representative types of tourism policies, crawls the comment data of Weibo users, analyzes users’ perception and emotional preference to the policy, and thus mines the social effect of various policies. Subsequently, by identifying the social effects of various policies as dependent variables, a binary logistic regression model is constructed to obtain the best combination of tourism promotion policies and promote the rapid revitalization of the cultural and tourism industry. Results: The results show that from the single policy, the social effect of the “safety” policy is the best. From the perspective of combination policies, the simultaneous release of “safety” policies and “economy” policies have the greatest social impact, which can dramatically accelerate the recovery of the cultural and tourism industry. Finally, this paper proposes suggestions for policy formulation to improve the ability of the cultural tourism industry to cope with crisis events. Conclusion: These results explain the perceived effects of the public on the government policies and can be used to judge whether the policies have been released in place. Based on the above results, corresponding suggestions are proposed as follows: 1) the combination of economic policies and security policies can achieve better results; and 2) the role of “opinion leaders” can be played to improve the perceived effect of policies.
Tinggui Chen; Lijuan Peng; Xiaohua Yin; Bailu Jing; Jianjun Yang; Guodong Cong; Gongfa Li. A Policy Category Analysis Model for Tourism Promotion in China During the COVID-19 Pandemic Based on Data Mining and Binary Regression. Risk Management and Healthcare Policy 2020, ume 13, 3211 -3233.
AMA StyleTinggui Chen, Lijuan Peng, Xiaohua Yin, Bailu Jing, Jianjun Yang, Guodong Cong, Gongfa Li. A Policy Category Analysis Model for Tourism Promotion in China During the COVID-19 Pandemic Based on Data Mining and Binary Regression. Risk Management and Healthcare Policy. 2020; ume 13 ():3211-3233.
Chicago/Turabian StyleTinggui Chen; Lijuan Peng; Xiaohua Yin; Bailu Jing; Jianjun Yang; Guodong Cong; Gongfa Li. 2020. "A Policy Category Analysis Model for Tourism Promotion in China During the COVID-19 Pandemic Based on Data Mining and Binary Regression." Risk Management and Healthcare Policy ume 13, no. : 3211-3233.
Background and Aim: The spread of the COVID-19 pandemic has led to a number of instances of large-scale panic buying. Taking the COVID-19 pandemic as an example, this paper explores the impact of panic in uncertain environments on panic buying behavior. Under certain circumstances, the spread of rumors about shortage of goods is likely to cause large-scale panic buying. This paper focuses on the study of such panic buying caused by online rumors. Methods: Firstly, based on the improved BA network, this paper constructs a directed network for public opinion communication and integrates an offline communication network to build a two-layer synchronous coupling network based on online and offline communications. Secondly, the individual decision model and the panic emotion transmission model under the uncertain environment are constructed. Netizens judge the authenticity of network information, determine their own panic degree according to the above two models, and judge whether they participate in the panic buying based on the above factors. Finally, the spread of the public opinion of goods buying under the panic state is simulated and analyzed. Results: The experimental results of the two-layer synchronous network that integrates offline interaction are significantly different from the results of pure online interaction, which increases the speed of public opinions spread after offline interaction and affects a wider range of groups. Under the condition of sufficient supplies, panic in local areas will not cause large-scale panic buying on the whole network. However, the results under the same parameters suggest that if there is a shortage of supplies, panic will spread quickly across the network, leading to large-scale panic buying. It is very important to ensure sufficient supply of materials at the beginning of the spread of rumors, which can reduce the number of buyers. However, if there is a shortage of goods before the panic dissipates in the later stage, there will still be a large-scale rush purchase. Conclusion: These results explain the reasons why it is difficult to stop the buying events in many areas under the COVID-19 pandemic. Under the uncertain environment, the panic caused by people’s fear of stock shortage promotes the occurrence of large-scale rush buying. Therefore, in the event of major public health events, ensuring adequate supply of materials is the top priority.
Qianqian Li; Tinggui Chen; Jianjun Yang; Guodong Cong. Based on Computational Communication Paradigm: Simulation of Public Opinion Communication Process of Panic Buying During the COVID-19 Pandemic. Psychology Research and Behavior Management 2020, ume 13, 1027 -1045.
AMA StyleQianqian Li, Tinggui Chen, Jianjun Yang, Guodong Cong. Based on Computational Communication Paradigm: Simulation of Public Opinion Communication Process of Panic Buying During the COVID-19 Pandemic. Psychology Research and Behavior Management. 2020; ume 13 ():1027-1045.
Chicago/Turabian StyleQianqian Li; Tinggui Chen; Jianjun Yang; Guodong Cong. 2020. "Based on Computational Communication Paradigm: Simulation of Public Opinion Communication Process of Panic Buying During the COVID-19 Pandemic." Psychology Research and Behavior Management ume 13, no. : 1027-1045.
Currently, China is in the period of social transformation. Such transformation continuously results in high group polarization behaviors, which attracts many attentions. In order to explore the evolutionary mechanism and formation process of group polarization behavior, this paper proposes a group polarization model which is integrated into the Susceptible-Infected-Recovered-Susceptible (SIRS) epidemic model. In this paper, firstly, the SIRS epidemic model and the factors of relationship strength are introduced based on the J-A model (proposed by Jager and Amblard) to enhance the information transmission and interaction among individuals. In addition, the BA network (proposed by Barabasi and Albert) model is used as the agent adjacency model due to its closeness to the real social network structure. After that, the Monte Carlo method is applied to conduct experimental simulation. Subsequently, this paper analyzes the simulation results in threefold: (1) comparison of polarization processes with and without integration of the SIRS epidemic model; (2) adjusting the immune recovery parameter γ and the relationship strength z to explore the role of these two parameters in the polarization process; and (3) comparing the polarization effects of different network structures. Through the experiments, we find that BA network is more polarized than small-world network in the same scale. Finally, corresponding measures are proposed to prevent and mitigate the occurrence of group polarization.
Tinggui Chen; Jiawen Shi; Jianjun Yang; Guodong Cong; Gongfa Li. Modeling Public Opinion Polarization in Group Behavior by Integrating SIRS-Based Information Diffusion Process. Complexity 2020, 2020, 1 -20.
AMA StyleTinggui Chen, Jiawen Shi, Jianjun Yang, Guodong Cong, Gongfa Li. Modeling Public Opinion Polarization in Group Behavior by Integrating SIRS-Based Information Diffusion Process. Complexity. 2020; 2020 ():1-20.
Chicago/Turabian StyleTinggui Chen; Jiawen Shi; Jianjun Yang; Guodong Cong; Gongfa Li. 2020. "Modeling Public Opinion Polarization in Group Behavior by Integrating SIRS-Based Information Diffusion Process." Complexity 2020, no. : 1-20.
The occurrence of popular social events causes fluctuations and changes of public emotions, while the rapid development of online social platforms and networks has made individual interactions more intense and further escalated public emotions into public opinion. However, there is a lack of consideration of individual emotions in the current research on online public opinion. Based on this, this paper firstly expounds the quantitative representation of attitude and emotion, analyzes the formation and propagation process of online public opinion by combining individual’s expression willingness, individual’s expression ability, attitude perception value, attitude change probability and other factors, and constructs a network public opinion propagation model that takes individual emotion into consideration. Finally, the main factors affecting the formation and propagation of network public opinion are discussed through simulation experiments. The results demonstrate that: (1) fear is conducive to the formation of online public opinion, but the speed is relatively slow; sadness is not conducive to the formation, but once enough people participate in the exchange of views, the formation of online public opinion will be faster; (2) the influence of online public opinion on individual emotions expands with the increase of the number of individual interactions; (3) different network structures impact differently on the propagation of public opinion. Among them, BA (BA network is a scale-free network model proposed by Barabasi and Albert in order to explain the generation mechanism of power law, BA model has two characteristics: growth and priority connection mechanism) and ER (ER network is a network with random connectivity proposed by Erdös-Renyi) random networks can promote the propagation of online public opinion, which is prone to “one-sided” online public opinion. WS small-world networks (proposed by Watts and Strogatz. It is a kind of network with short average path length and high clustering coefficient) and fully-connected networks have an inhibitory effect on the spread of online public opinion, easily maintaining the multi-dimensional nature of online public opinion.
Peihua Fu; Bailu Jing; Tinggui Chen; Jianjun Yang; Guodong Cong. Modeling Network Public Opinion Propagation with the Consideration of Individual Emotions. International Journal of Environmental Research and Public Health 2020, 17, 6681 .
AMA StylePeihua Fu, Bailu Jing, Tinggui Chen, Jianjun Yang, Guodong Cong. Modeling Network Public Opinion Propagation with the Consideration of Individual Emotions. International Journal of Environmental Research and Public Health. 2020; 17 (18):6681.
Chicago/Turabian StylePeihua Fu; Bailu Jing; Tinggui Chen; Jianjun Yang; Guodong Cong. 2020. "Modeling Network Public Opinion Propagation with the Consideration of Individual Emotions." International Journal of Environmental Research and Public Health 17, no. 18: 6681.
During the COVID-19 pandemic, social education has shifted from face to face to online in order to avoid large gatherings and crowds for blocking the transmission of the virus. To analyze the impact of virus on user experience and deeply retrieve users’ requirements, this paper constructs a reasonable evaluation index system through obtaining user reviews about seven major online education platforms before and after the outbreak of COVID-19, and by combining the emotional analysis, hot mining technology, as well as relevant literature. At the same time, the variation coefficient method is chosen to weigh each index based on the difference of index values. Furthermore, this paper adopts the comprehensive evaluation method to analyze user experience before and after the outbreak of COVID-19, and finally finds out the change of users’ concerns regarding the online education platform. In terms of access speed, reliability, timely transmission technology of video information, course management, communication and interaction, and learning and technical support, this paper explores the supporting abilities and response levels of online education platforms during COVID-19, and puts forward corresponding measures to improve how these platforms function.
Tinggui Chen; Lijuan Peng; Bailu Jing; Chenyue Wu; Jianjun Yang; Guodong Cong. The Impact of the COVID-19 Pandemic on User Experience with Online Education Platforms in China. Sustainability 2020, 12, 7329 .
AMA StyleTinggui Chen, Lijuan Peng, Bailu Jing, Chenyue Wu, Jianjun Yang, Guodong Cong. The Impact of the COVID-19 Pandemic on User Experience with Online Education Platforms in China. Sustainability. 2020; 12 (18):7329.
Chicago/Turabian StyleTinggui Chen; Lijuan Peng; Bailu Jing; Chenyue Wu; Jianjun Yang; Guodong Cong. 2020. "The Impact of the COVID-19 Pandemic on User Experience with Online Education Platforms in China." Sustainability 12, no. 18: 7329.
The outbreak of Corona Virus Disease 2019 (COVID-19) in various countries at the end of last year has transferred traditional face-to-face teaching to online education platforms, which directly affects the quality of education. Taking user satisfaction on online education platforms in China as the research object, this paper uses a questionnaire survey and web crawler to collect experience data of online and offline users, constructs a customer satisfaction index system by analyzing emotion and the existing literature for quantitative analysis, and builds aback propagation (BP) neural network model to forecast user satisfaction. The conclusion shows that users’ personal factors have no direct influence on user satisfaction, while platform availability has the greatest influence on user satisfaction. Finally, suggestions on improving the online education platform are given to escalate the level of online education during the COVID-19 pandemic, so as to promote the reform of information-based education.
Tinggui Chen; Lijuan Peng; Xiaohua Yin; Jingtao Rong; Jianjun Yang; Guodong Cong. Analysis of User Satisfaction with Online Education Platforms in China during the COVID-19 Pandemic. Healthcare 2020, 8, 200 .
AMA StyleTinggui Chen, Lijuan Peng, Xiaohua Yin, Jingtao Rong, Jianjun Yang, Guodong Cong. Analysis of User Satisfaction with Online Education Platforms in China during the COVID-19 Pandemic. Healthcare. 2020; 8 (3):200.
Chicago/Turabian StyleTinggui Chen; Lijuan Peng; Xiaohua Yin; Jingtao Rong; Jianjun Yang; Guodong Cong. 2020. "Analysis of User Satisfaction with Online Education Platforms in China during the COVID-19 Pandemic." Healthcare 8, no. 3: 200.
With the rapid development of “we media” technology, external information about the same sudden hot social event is often involved repetitiously, leading to frequent public opinion reversal. However, the phenomenon of public opinion reversal process usually has a long-lasting duration and spreads wide, making the event itself attract the widespread attention of ordinary people. Focusing on the public opinion reversal process of sudden social hot topic (a popular and widely discussed issue), this paper firstly identifies the internal and external factors that affect the reversal, namely individual internal characteristics and external intervention information. Secondly, information intensity and the amount of information perceived by individuals are introduced to describe the impact of external intervention information on the public opinion reversal. Thirdly, the parameters of individual attention and conservation are used to describe the process of individual’s selection of external information, so as to reveal the influence of the internal characteristics on public opinion reversal, and then build a public opinion reversal model. Fourthly, the effects of information intensity and individual attention, as well as individual conservation on the process of public opinion reversal are analyzed by simulation experiment. Simulation results show that: (1) the intensity of external intervention information affects the direction and degree of public opinion reversal; (2) when individual conservation is strong or individual attention is weak, even if external intervention information is strong, there will still be no obvious reversal of public opinion. Subsequently, the rationality and effectiveness of the proposed model are verified by a real case. Finally, some recommendations and policy implications are also given.
Tinggui Chen; Yulong Wang; Jianjun Yang; Guodong Cong. Modeling Public Opinion Reversal Process with the Considerations of External Intervention Information and Individual Internal Characteristics. Healthcare 2020, 8, 160 .
AMA StyleTinggui Chen, Yulong Wang, Jianjun Yang, Guodong Cong. Modeling Public Opinion Reversal Process with the Considerations of External Intervention Information and Individual Internal Characteristics. Healthcare. 2020; 8 (2):160.
Chicago/Turabian StyleTinggui Chen; Yulong Wang; Jianjun Yang; Guodong Cong. 2020. "Modeling Public Opinion Reversal Process with the Considerations of External Intervention Information and Individual Internal Characteristics." Healthcare 8, no. 2: 160.
The study of vehicular networks has attracted great interest in academia and the industry. In the broad area, connected vehicles and autonomous driving are technologies based on wireless data communication between vehicles or between vehicles and infrastructures. A Vehicle-to-Infrastructure (V2I) system consists of communications and computing over vehicles and related infrastructures. In such a system, wireless sensors are installed in some selected points along roads or driving areas. In autonomous driving, it is crucial for a vehicle to figure out the ideal routes by the communications between its equipped sensors and infrastructures then the vehicle is automatically moving along the routes. In this paper, we propose a Bezier curve based recursive algorithm, which effectively creates routes for vehicles through the communication between the OBU (On Board Unit) and RSUs (Road Side). In addition, this approach generates a very low overhead. We conduct simulations to test the proposed algorithm in various situations. The experiment results demonstrate that our algorithm creates almost ideal routes.
Jianjun Yang; Tinggui Chen; Bryson Payne; Ping Guo; Yanping Zhang; Juan Guo. Generating routes for autonomous driving in vehicle-to-infrastructure communications. Digital Communications and Networks 2020, 6, 444 -451.
AMA StyleJianjun Yang, Tinggui Chen, Bryson Payne, Ping Guo, Yanping Zhang, Juan Guo. Generating routes for autonomous driving in vehicle-to-infrastructure communications. Digital Communications and Networks. 2020; 6 (4):444-451.
Chicago/Turabian StyleJianjun Yang; Tinggui Chen; Bryson Payne; Ping Guo; Yanping Zhang; Juan Guo. 2020. "Generating routes for autonomous driving in vehicle-to-infrastructure communications." Digital Communications and Networks 6, no. 4: 444-451.
Social conflicts occur frequently during the social transition period and the polarization of public opinion happens occasionally. By introducing the social preference theory, the target of this paper is to reveal the micro-interaction mechanism of public opinion polarization. Firstly, we divide the social preferences of Internet users (network nodes) into three categories: egoistic, altruistic, and fair preferences, and adopt the revenue function to define the benefits obtained by individuals with different preferences among their interaction process so as to analyze their decision-making behaviors driven by the revenue. Secondly, the revenue function is used to judge the exit rules of nodes in a network, and then a dynamic network of spreading public opinion with the node (individual) exit mechanism is built based on a BA scale-free network. Subsequently, the influences of different social preferences, as well as individual revenue on the effect of public opinion polarization, are analyzed through simulation experiments. The simulation results show that (1) Different social preferences demonstrate different influences on the evolution of public opinions, (2) Individuals tend to interact with ones with different preferences, (3) The network with a single preference or a high aggregation is more likely to form public opinion polarization. Finally, the practicability and effectiveness of the proposed model are verified by a real case.
Tinggui Chen; Qianqian Li; Peihua Fu; Jianjun Yang; Chonghuan Xu; Guodong Cong; Gongfa Li. Public Opinion Polarization by Individual Revenue from the Social Preference Theory. International Journal of Environmental Research and Public Health 2020, 17, 946 .
AMA StyleTinggui Chen, Qianqian Li, Peihua Fu, Jianjun Yang, Chonghuan Xu, Guodong Cong, Gongfa Li. Public Opinion Polarization by Individual Revenue from the Social Preference Theory. International Journal of Environmental Research and Public Health. 2020; 17 (3):946.
Chicago/Turabian StyleTinggui Chen; Qianqian Li; Peihua Fu; Jianjun Yang; Chonghuan Xu; Guodong Cong; Gongfa Li. 2020. "Public Opinion Polarization by Individual Revenue from the Social Preference Theory." International Journal of Environmental Research and Public Health 17, no. 3: 946.
It is common that many roads in disaster areas are damaged and obstructed after sudden-onset disasters. The phenomenon often comes with escalated traffic deterioration that raises the time and cost of emergency supply scheduling. Fortunately, repairing road network will shorten the time of in-transit distribution. In this paper, according to the characteristics of emergency supplies distribution, an emergency supply scheduling model based on multiple warehouses and stricken locations is constructed to deal with the failure of part of road networks in the early postdisaster phase. The detailed process is as follows. When part of the road networks fail, we firstly determine whether to repair the damaged road networks, and then a model of reliable emergency supply scheduling based on bi-level programming is proposed. Subsequently, an improved artificial bee colony algorithm is presented to solve the problem mentioned above. Finally, through a case study, the effectiveness and efficiency of the proposed model and algorithm are verified.
Tinggui Chen; Shiwen Wu; Jianjun Yang; Guodong Cong; Gongfa Li. Modeling of Emergency Supply Scheduling Problem Based on Reliability and Its Solution Algorithm under Variable Road Network after Sudden-Onset Disasters. Complexity 2020, 2020, 1 -15.
AMA StyleTinggui Chen, Shiwen Wu, Jianjun Yang, Guodong Cong, Gongfa Li. Modeling of Emergency Supply Scheduling Problem Based on Reliability and Its Solution Algorithm under Variable Road Network after Sudden-Onset Disasters. Complexity. 2020; 2020 ():1-15.
Chicago/Turabian StyleTinggui Chen; Shiwen Wu; Jianjun Yang; Guodong Cong; Gongfa Li. 2020. "Modeling of Emergency Supply Scheduling Problem Based on Reliability and Its Solution Algorithm under Variable Road Network after Sudden-Onset Disasters." Complexity 2020, no. : 1-15.
Emergency logistics plays an important role in the rescue process after sudden disasters. However, in the process of emergency logistics activities, risks may arise due to scheduling problems or insufficient supply of warehouse stocks, resulting in an insufficient rescue capacity. In addition, the risk of emergency logistics is random and may exist in a certain link or throughout the whole rescue process of emergency logistics. Consequently, the disaster site may be invaded by sudden disaster risk due to the lack of necessary material supplies. The entire emergency logistics system may be destroyed and cause even greater losses as well. Based on this phenomenon, this paper introduces reliability factors of materials and combines the complex network theory to build an emergency logistics network and analyze the emergency logistics risk propagation mechanism. This paper firstly builds an emergency logistics network based on complex network theory. Then, it combines the improved epidemic model to analyze the influencing factors of risk propagation in the emergency logistics network. Finally, this paper probes into the emergency logistics risk propagation mechanisms and processes in terms of network type, material reliability, rescue speed, etc. Furthermore, this paper identifies key factors for risk control and proposes countermeasures to further spread risks, thereby reducing the risk to loss of economic life.
Tinggui Chen; Shiwen Wu; Jianjun Yang; Guodong Cong. Risk Propagation Model and Its Simulation of Emergency Logistics Network Based on Material Reliability. International Journal of Environmental Research and Public Health 2019, 16, 4677 .
AMA StyleTinggui Chen, Shiwen Wu, Jianjun Yang, Guodong Cong. Risk Propagation Model and Its Simulation of Emergency Logistics Network Based on Material Reliability. International Journal of Environmental Research and Public Health. 2019; 16 (23):4677.
Chicago/Turabian StyleTinggui Chen; Shiwen Wu; Jianjun Yang; Guodong Cong. 2019. "Risk Propagation Model and Its Simulation of Emergency Logistics Network Based on Material Reliability." International Journal of Environmental Research and Public Health 16, no. 23: 4677.
Nowadays, hot issues are likely become bipolar or multipolar after heated discussion on the Internet. This article is focused on the study of the polarization phenomenon and establishes a public opinion polarization model with the considerations of individual heterogeneity and dynamic conformity. At first, this article introduces the dynamic changing function of an individual’s conformity tendency to other’s attitudes in the interaction process. It further defines the influential weight between different interactive individuals, and expands the interactive individual from complete homogeneity to initial attitude heterogeneity, and finally, conformity heterogeneity. Then, through simulation experiments, we find that the degree of changing in individual attitude is limited. That is, it is difficult for the individuals who have one directional attitude at the initial time to change into another opposite attitude through interaction. In addition, individuals with low conformity within a certain threshold are more likely to form polarization. Finally, the rationality and effectiveness of the proposed model are verified by the typical case “Mimeng Event”.
Tinggui Chen; Qianqian Li; Jianjun Yang; Guodong Cong. Modeling of the Public Opinion Polarization Process with the Considerations of Individual Heterogeneity and Dynamic Conformity. Mathematics 2019, 7, 917 .
AMA StyleTinggui Chen, Qianqian Li, Jianjun Yang, Guodong Cong. Modeling of the Public Opinion Polarization Process with the Considerations of Individual Heterogeneity and Dynamic Conformity. Mathematics. 2019; 7 (10):917.
Chicago/Turabian StyleTinggui Chen; Qianqian Li; Jianjun Yang; Guodong Cong. 2019. "Modeling of the Public Opinion Polarization Process with the Considerations of Individual Heterogeneity and Dynamic Conformity." Mathematics 7, no. 10: 917.
As part of the next generation Internet, Wireless Mesh Networks have emerged as a key technology to deliver Internet broadband access, wireless local area network coverage and network connectivity at low costs. The capacity of a wireless mesh network is improved by equipping mesh nodes with multi-radios tuned to non-overlapping channels. Hence the data forwarding between two nodes has multiple selections of links and the bandwidth between the pair of nodes varies dynamically. The new technology makes mesh nodes cognitive, thus a mesh node is able to adopt machine learning mechanisms to choose the possible best next hop which has maximum bandwidth when it intends to forward data. In this paper, we present a new forwarding algorithm by which a forwarding node dynamically select its next hop with highest potential bandwidth capacity to resume communication based on learning algorithm. The efficiency of this approach is that a node only maintains three past status, and then it is able to learn and predict the potential bandwidth capacities of its links. Then, the node selects the next hop with potential maximal link bandwidth. Additionally, a geometrical based algorithm is developed to let the forwarding node figure out the best forwarding region in order to avoid flooding. Simulations demonstrate that our approach significantly outperforms peer algorithms.
Jianjun Yang; Ju Shen; Mengyi Ying. Forwarding with Prediction over Machine Learning based Nodes in Wireless Mesh Networks. Transactions on Networks and Communications 2018, 6, 33 .
AMA StyleJianjun Yang, Ju Shen, Mengyi Ying. Forwarding with Prediction over Machine Learning based Nodes in Wireless Mesh Networks. Transactions on Networks and Communications. 2018; 6 (6):33.
Chicago/Turabian StyleJianjun Yang; Ju Shen; Mengyi Ying. 2018. "Forwarding with Prediction over Machine Learning based Nodes in Wireless Mesh Networks." Transactions on Networks and Communications 6, no. 6: 33.
In the recent cellular network technologies, relay stations extend cell coverage and enhance signal strength for mobile users. However, busy traffic makes the relay stations in hot area run out of energy quickly. Energy is a very important factor in the forwarding of cellular network since mobile users(cell phones) in hot cells often suffer from low throughput due to energy lack problems. In many situations, the energy lack problems take place because the energy loading is not balanced. In this paper, we present a prediction based forwarding algorithm to let a mobile node dynamically select the next relay station with highest potential energy capacity to resume communication. Key to this strategy is that a relay station only maintains three past status, and then it is able to predict the potential energy capacity. Then, the node selects the next hop with potential maximal energy. Moreover, a location based algorithm is developed to let the mobile node figure out the target region in order to avoid flooding. Simulations demonstrate that our approach significantly increase the aggregate throughput and decrease the delay in cellular network environment.
Jian-Jun Yang; Ju Shen. Prediction Based Energy Balancing Forwarding in Cellular Networks. ITM Web of Conferences 2017, 12, 3017 .
AMA StyleJian-Jun Yang, Ju Shen. Prediction Based Energy Balancing Forwarding in Cellular Networks. ITM Web of Conferences. 2017; 12 ():3017.
Chicago/Turabian StyleJian-Jun Yang; Ju Shen. 2017. "Prediction Based Energy Balancing Forwarding in Cellular Networks." ITM Web of Conferences 12, no. : 3017.