Exploring Influencing Factors of Chronic Obstructive Pulmonary Disease Based on Elastic Net and Bayesian Network

This study aimed to construct Bayesian networks(BNs) to analyze the network relationship between those influencing factors and COPD, and to explore their intensity of effect on COPD through network reasoning. Elastic Net and Max-Min Hill-Climbing(MMHC) hybrid algorithm were adopted to screen the variables on the monitoring data of COPD among residents in Shanxi Province, China from 2014 to 2015, and construct BNs respectively. After variables selection by Elastic Net, 10 variables closely related to COPD were selected finally. The BNs constructed by MMHC showed that smoking status, household air pollution, family history, cough, air hunger or dyspnea were directly related to COPD, and Gender was indirectly linked to COPD through smoking status. Moreover, smoking status, household air pollution and family history were the parent nodes of COPD, and cough, air hunger or dyspnea represented the child nodes of COPD. In other words, smoking status, household air pollution and family history were related to the occurrence of COPD, and COPD would make patients’ cough, air hunger or dyspnea worse. Generally speaking, BNs could reveal the complex network relationship between COPD and its relevant factors well, making it more convenient to carry out targeted prevention and control of COPD.

Simulation Research on Risks of Entrepreneurship Platform Organization Complex Network

The risks of entrepreneurship platform are considered one of the most significant factors that affect regional economic development. However, the complexity of the constitutive relationship and the dynamics of the research process have made it difficult for studies to analyse the evolution and risks from the quantitative perspective. According to the analysis perspective of complex networks, this study determined the coupling relationship between the entrepreneurship platform network structure and complex network model. With the results studied and described in the paper, this study had constructed a platform structure model portraying the evolution process of the platform structure under two types of risks by using the simulation method. Three main conclusions are being drawn from the study: Firstly, endogenous and exogenous risks showed substantial results in affecting the changes in microentities and network relationship of enterprises within the platform, causing the robustness of platform to risk to differ significantly. Secondly, based on exogenous risks, the robustness distribution scaling from highest to lowest among three types of platforms studied is hub-and-spoke > mixed > market. Lastly, based on endogenous risk, the robustness distribution scaling from highest to lowest among the three types of platform studies is market > mixed > hub-and-spoke.

Single, Repeat, and Poly Intimate Partner Violence Victimization among Women at a College Campus: Extending Research Through the Inclusion of Technology-Facilitated Violence and Examining Key Social Determinants for Intimate Partner Violence Prevention

This study examines intimate partner violence (IPV) victimization (i.e., technology-facilitated and in-person psychological, physical, and sexual) and polyvictimization, along with the role of social support and other factors in influencing these experiences. Using a sample of college women in intimate relationships in the past year ( n = 265), findings revealed that social support was important in predicting IPV victimizations, with less prosocial support contributing to more frequent victimization for specific IPV forms and polyvictimization. The same support features emerged as significant for repeat technology-facilitated and repeat psychological IPV (i.e., social network relationship support), and for repeat physical and repeat sexual IPV (i.e., family connectedness), suggesting certain forms share commonalities. In the polyvictimization model, both social support measures were significant. The implications for IPV research and violence prevention are discussed.

Sustainable Supply Chain Management in the Route for a Circular Economy: An Integrative Literature Review

Sustainable supply chain management (SSCM) considers social, environmental, and economic dimensions of sustainability. In the context of the pandemic, organizations must face consequences striking the wider dimensions of sustainability. Thus, after the COVID-19 pandemic, how will the value chains collaborate for the transition from a traditional (linear) to a Circular Economy? From this question, in this paper, we analyze the international papers that connect sustainable supply chain management (SSCM) with circular economy (CE). We conducted an Integrative Literature Review based on the Web of Science and Scopus databases from 2010 to 2020, using the Methodi Ordinatio protocol to classify the papers. The 37 best-ranked papers were analyzed thoroughly. The results show the prominent authors, institutions, the network relationship between authors, the evolution of publications, and the leading journals. The content of these articles was categorized and discussed about the changes in the way products are manufactured, distributed, consumed, and recovered. The integration of CE principles in SSCM has been evaluated as having potential utility for industries, cities, and businesses in general. Finally, an agenda was identified with suggestions for further research, which can aid researchers and practitioners acting in this field. Managers can obtain insights to improve supply chain sustainability and consequently respond to the challenges imposed by the current pandemic.

Exploring Related Factors of Chronic Obstructive Pulmonary Disease Based on Elastic Net and Bayesian Network

Objective This study aimed to construct Bayesian networks to analyze the network relationship between COPD and its related factors, and to explore the influencing intensity on COPD through network reasoning. Method Firstly Elastic Net and MMHC hybrid algorithm were adopted to screen the variables of the data of COPD in Shanxi Province from 2014 to 2015 and construct Bayesian networks respectively, and the parameters were estimated by maximum likelihood estimation. Results After feature selection by Elastic Net, 10 variables closely related to COPD finally entered the model. The COPD Bayesian networks constructed by MMHC algorithm showed that smoking status, household air pollution, family history, cough, air hunger or dyspnea were directly related to COPD, in which smoking status, household air pollution and family history were the parent nodes of COPD, and cough, air hunger or dyspnea represented the child nodes of COPD. In other words, smoking status, household air pollution, family history were related to the occurrence of COPD, and COPD would affect cough, air hunger or dyspnea. Gender was indirectly linked to COPD through smoking status. Conclusion Using Elastic Net to knock out some weakly-associated influencing factors of COPD in the variable screening stage, Bayesian networks could reveal the complex network relationship between COPD and its relevant factors well, making it more convenient to carry out targeted prevention and control of COPD. As such, Bayesian networks enjoyed a good prospect of application in analyzing disease-related factors.

Music Network Data Analysis Based on ISOMAP Algorithm Model

The development of music is a tortuous process, and the network relationship between each genre and each artist is intricate. In order to have a better understanding of the history of music, this paper tells the stories hidden in the history of music by means of data processing. Firstly, this paper establishes a model to evaluate the similarity between music by using ISOMAP algorithm. At the same time, the forest evolution model was established to mark the most revolutionary musical characters. Finally, using the Page-Rank algorithm, we get the founders of several music genres. It turns out that the figures who led the development of music don’t coincide with the figures who revolutionized music. Through the analysis of this paper, we can more clearly understand the development of music and the evolution of genres.

Research on the Influence Mechanism and Configuration Path of Network Relationship Characteristics on SMEs’ Innovation—The Mediating Effect of Supply Chain Dynamic Capability and the Moderating Effect of Geographical Proximity

Innovation is the continuous source of power for the survival and development of SMEs, but the complexity of innovation and the limitation of resources make SMEs trapped in the dilemma of “innovation difficulty”. A moderated mediating model was constructed based on social network theory and resource view, and fuzzy set qualitative comparative analysis (fsQCA) was used to empirically study the influence mechanism between network relationship characteristics and SMEs’ innovation and the configuration path to achieve SMEs’ high innovation performance. The results show that the characteristics of network relationships positively affect the innovation performance of SMEs. Supply chain dynamic capability plays an intermediary role between network relationships and SMEs’ innovation relationships. Different geographical proximity levels of the supply chain lead to different coordination interaction and knowledge sharing efficiency between upstream and downstream, which not only positively moderates the relationship between supply chain dynamic capability and SMEs’ innovation performance, but also moderates its mediating effect. Furthermore, the fs QCA analysis results show three configurations for SMEs’ high innovation performance based on the characteristics of network relations: geographic proximity regulating type, network relationship dominant type, and dynamic coordination and integration type.

Data Science Talents Mining from Online Recruitment Market in China Based on Data Mining Technique

As the country implements the big data strategy and accelerates the construction of a digital China, data science has entered a new and dynamic era, and the demand for data science talents in all walks of life is increasing. Many talent training departments have added undergraduates or degrees to data science talents, but it is still unclear whether they can meet social and economic development needs. This article aims to improve the quality and adaptability of data science talent training and conduct an in-depth analysis of the demand for data science talents. The technology used in this article is data mining technology. The data information of data science talents is crawled out of the demand information of data science talents on the recruitment website. The core content of network relationship visualization is proposed and analyzed through machine learning methods and text subject word extraction models. Achieve a comprehensive exploration of the demand for data science talents and provide a reference for talent training units to formulate data science talent training models.

Using DANP-mV Model to Improve the Paid Training Measures for Travel Agents amid the COVID-19 Pandemic

Due to the COVID-19 pandemic bringing travel to a standstill, an initiative of paid training for travel agencies was launched by the government. The purpose of this research is to improve the effectiveness of this measure, which is fundamental to the next tourism crisis management. Based on the related theories of tourism crisis management, organizational learning, and behavior adaption, the DANP-mV model developed an evaluation system for examining the training measure. The result of an influential network relationship map (INRM) shows that using influential effects, the “policy object” dimension and its criterion “subsidiary” should be the first improvement priority. To effectively achieve the aspiration level, the gap values point out “training courses” dimension and its criterion “excellent services” are first to improve. An action plan is produced containing all the findings and made available for easy indexing. It may contribute to the current measure improvements. Therefore, this innovative approach could help decision makers with tourism crisis policy making and help the sector with evolving learning readiness to remain sustainable.