As a typical case of the optimal planning for the provision of restricted medical resources, widespread vaccination is considered an effective and sustainable way to prevent and control large-scale novel coronavirus disease 2019 (COVID-19) outbreaks. However, an initial supply shortage of vaccines is inevitable because of the narrow production and logistical capacity. This work focuses on the multi-type vaccine resource allocation problem in a two-dose vaccination campaign under limited supply. To address this issue, we extended an age-stratified susceptible, exposed, infectious, and recovered (SEIR) epidemiological model to incorporate a two-dose vaccination campaign involving multiple vaccine types to fully characterize the various stages of infection and vaccination. Afterward, we integrated the proposed epidemiological model into a nonlinear programming (NLP) model to determine the optimal allocation strategy under supply capacity and vaccine hesitancy constraints with the goal of minimizing the cumulative number of deaths due to the pandemic over the entire planning horizon. A case study based on real-world data from the initial mass vaccination campaign against COVID-19 in the Midlands, England, was taken to validate the applicability of our model. Then, we performed a comparative study to demonstrate the performance of the proposed method and conducted an extensive sensitivity analysis on critical model parameters. Our results indicate that prioritizing the allocation of vaccines to elderly persons is an effective strategy for reducing COVID-19-related fatalities. Furthermore, we found that vaccination alone will not be sufficient for epidemic control in the short term, and appropriate non-pharmacological interventions are still important for effective viral containment during the initial vaccine rollout. The results also showed that the relative efficacy of the first dose is a vital factor affecting the optimal interval between doses. It is always best to complete the two-dose vaccination schedule as soon as possible when the relative efficacy of the first dose is low. Conversely, delaying the second dose of a vaccine as long as possible to increase the proportion of the population vaccinated with a single dose tends to be more favorable when the relative efficacy of the first dose is high. Finally, our proposed model is general and easily extendable to the study of other infectious disease outbreaks and provides important implications for public health authorities seeking to develop effective vaccine allocation strategies for tackling possible future pandemics.
{"title":"Optimal Allocation of Multi-Type Vaccines in a Two-Dose Vaccination Campaign for Epidemic Control: A Case Study of COVID-19","authors":"Jin Zhu, Qing Wang, Min Huang","doi":"10.3390/systems12080286","DOIUrl":"https://doi.org/10.3390/systems12080286","url":null,"abstract":"As a typical case of the optimal planning for the provision of restricted medical resources, widespread vaccination is considered an effective and sustainable way to prevent and control large-scale novel coronavirus disease 2019 (COVID-19) outbreaks. However, an initial supply shortage of vaccines is inevitable because of the narrow production and logistical capacity. This work focuses on the multi-type vaccine resource allocation problem in a two-dose vaccination campaign under limited supply. To address this issue, we extended an age-stratified susceptible, exposed, infectious, and recovered (SEIR) epidemiological model to incorporate a two-dose vaccination campaign involving multiple vaccine types to fully characterize the various stages of infection and vaccination. Afterward, we integrated the proposed epidemiological model into a nonlinear programming (NLP) model to determine the optimal allocation strategy under supply capacity and vaccine hesitancy constraints with the goal of minimizing the cumulative number of deaths due to the pandemic over the entire planning horizon. A case study based on real-world data from the initial mass vaccination campaign against COVID-19 in the Midlands, England, was taken to validate the applicability of our model. Then, we performed a comparative study to demonstrate the performance of the proposed method and conducted an extensive sensitivity analysis on critical model parameters. Our results indicate that prioritizing the allocation of vaccines to elderly persons is an effective strategy for reducing COVID-19-related fatalities. Furthermore, we found that vaccination alone will not be sufficient for epidemic control in the short term, and appropriate non-pharmacological interventions are still important for effective viral containment during the initial vaccine rollout. The results also showed that the relative efficacy of the first dose is a vital factor affecting the optimal interval between doses. It is always best to complete the two-dose vaccination schedule as soon as possible when the relative efficacy of the first dose is low. Conversely, delaying the second dose of a vaccine as long as possible to increase the proportion of the population vaccinated with a single dose tends to be more favorable when the relative efficacy of the first dose is high. Finally, our proposed model is general and easily extendable to the study of other infectious disease outbreaks and provides important implications for public health authorities seeking to develop effective vaccine allocation strategies for tackling possible future pandemics.","PeriodicalId":36394,"journal":{"name":"Systems","volume":"59 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141938282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the outbreak and evolution of the pandemic worldwide, the financial market has experienced unprecedented shocks and adjustments, and the volatility and correlation of the stock market, as an important indicator of economic activities, have shown new features and trends during the pandemic. Based on the CSI 300 Index, we construct a three-stage sequential network representing the pre-pandemic, pandemic, and post-relaxation phases. We investigate the evolving dynamics and resilience of the network, forecasting potential future connections, thus offering fresh insights into comprehending market recovery. Our findings unveil that the market adapts dynamically to the pandemic’s progression, witnessing an overall augmentation in network interconnectedness. While the financial sector maintains its pivotal role, the influence of non-financial sectors experiences an upsurge. Despite the network demonstrating poor stability and heavy reliance on key nodes, there exists a positive recovery trajectory. Non-financial sectors such as energy and transportation emerge as pivotal catalysts for market rejuvenation. We provide suggestions for government regulators and investors, providing strong support for optimizing the market structure and promoting the long-term healthy development of the market.
{"title":"Complex-Systems Analysis of the CSI 300 Index: Evolution, Resilience, and Prediction in Stock Correlation Network","authors":"Xinyuan Luo, Jian Yin, Danqi Wei","doi":"10.3390/systems12080285","DOIUrl":"https://doi.org/10.3390/systems12080285","url":null,"abstract":"With the outbreak and evolution of the pandemic worldwide, the financial market has experienced unprecedented shocks and adjustments, and the volatility and correlation of the stock market, as an important indicator of economic activities, have shown new features and trends during the pandemic. Based on the CSI 300 Index, we construct a three-stage sequential network representing the pre-pandemic, pandemic, and post-relaxation phases. We investigate the evolving dynamics and resilience of the network, forecasting potential future connections, thus offering fresh insights into comprehending market recovery. Our findings unveil that the market adapts dynamically to the pandemic’s progression, witnessing an overall augmentation in network interconnectedness. While the financial sector maintains its pivotal role, the influence of non-financial sectors experiences an upsurge. Despite the network demonstrating poor stability and heavy reliance on key nodes, there exists a positive recovery trajectory. Non-financial sectors such as energy and transportation emerge as pivotal catalysts for market rejuvenation. We provide suggestions for government regulators and investors, providing strong support for optimizing the market structure and promoting the long-term healthy development of the market.","PeriodicalId":36394,"journal":{"name":"Systems","volume":"15 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141938285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
COVID-19 vaccination status has become a significant factor influencing carbon emissions in recent years. This paper explores the relationship between vaccination programs and CO2 emissions to provide scientific support for future emergency management. The study utilizes daily carbon emissions data and daily vaccination program data from six sectors within the European Union. It compares the accuracy of various machine learning models by incorporating 11 economic control variables. Additionally, it quantitatively decomposes the contribution of each variable to carbon emissions during the pandemic using SHAP values. The findings indicate that the LightGBM model predicts carbon emissions much more accurately than other models. Furthermore, COVID-19-related variables, such as daily vaccination volumes and cumulative vaccination totals, are identified as significant factors affecting carbon emissions.
{"title":"Measuring the Impact of COVID-19 Vaccination Rates on Carbon Emissions Using LightGBM Model: Evidence from the EU Region","authors":"Xinran Yue, Yan Li","doi":"10.3390/systems12080284","DOIUrl":"https://doi.org/10.3390/systems12080284","url":null,"abstract":"COVID-19 vaccination status has become a significant factor influencing carbon emissions in recent years. This paper explores the relationship between vaccination programs and CO2 emissions to provide scientific support for future emergency management. The study utilizes daily carbon emissions data and daily vaccination program data from six sectors within the European Union. It compares the accuracy of various machine learning models by incorporating 11 economic control variables. Additionally, it quantitatively decomposes the contribution of each variable to carbon emissions during the pandemic using SHAP values. The findings indicate that the LightGBM model predicts carbon emissions much more accurately than other models. Furthermore, COVID-19-related variables, such as daily vaccination volumes and cumulative vaccination totals, are identified as significant factors affecting carbon emissions.","PeriodicalId":36394,"journal":{"name":"Systems","volume":"91 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141968993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jinghua Zhao, Ruishu Zhang, Zhuang Wang, Shaoyun Cui
Decision-making regarding the low-carbon supply chain, considering corporate social responsibility (CSR) and the heterogeneous preferences of consumers, has become an urgent topic to be explored. This paper explores the decision-making problem of a low-carbon supply chain considering the heterogeneous preferences of consumers under different CSR situations, analyzes the influence of important parameters on each equilibrium solution, compares the size relationship of each equilibrium solution under different CSR situations, and verifies the conclusions obtained through numerical simulation. Then, based on the obtained equilibrium solution, a CSR evolutionary game model of the low-carbon supply chain is constructed, and the evolutionary stability strategies of the two sides on the CSR game are solved. Finally, the evolutionary trajectory of the game system is intuitively presented using a simulation method, and the influence of the main parameters on the evolutionary trends of the two sides is analyzed. The findings are as follows: (1) When both manufacturers and retailers undertake CSR, the retail price and wholesale price are their lowest, while carbon emission reduction, total market demand, manufacturer utility, retailer utility, and supply chain total utility are the highest. (2) When a company undertakes CSR, carbon emission reduction, total market demand, manufacturer utility, retailer utility, and supply chain total utility all increase with the increase in the CSR degree of the company and the ratio of the potential scale of low-carbon consumers to the potential scale of ordinary consumers. (3) The evolutionary stability strategy for both manufacturers and retailers is to undertake CSR. In addition, the initial proportion of manufacturers and retailers that undertake CSR, the low-carbon preference of low-carbon consumers, and the increase in the ratio of the potential scale of low-carbon consumers to the potential scale of ordinary consumers can encourage both members of the supply chain to undertake CSR.
{"title":"Low-Carbon Supply Chain Decision-Making and CSR Strategy Evolution Analysis Considering Heterogeneous Consumer Preferences","authors":"Jinghua Zhao, Ruishu Zhang, Zhuang Wang, Shaoyun Cui","doi":"10.3390/systems12080283","DOIUrl":"https://doi.org/10.3390/systems12080283","url":null,"abstract":"Decision-making regarding the low-carbon supply chain, considering corporate social responsibility (CSR) and the heterogeneous preferences of consumers, has become an urgent topic to be explored. This paper explores the decision-making problem of a low-carbon supply chain considering the heterogeneous preferences of consumers under different CSR situations, analyzes the influence of important parameters on each equilibrium solution, compares the size relationship of each equilibrium solution under different CSR situations, and verifies the conclusions obtained through numerical simulation. Then, based on the obtained equilibrium solution, a CSR evolutionary game model of the low-carbon supply chain is constructed, and the evolutionary stability strategies of the two sides on the CSR game are solved. Finally, the evolutionary trajectory of the game system is intuitively presented using a simulation method, and the influence of the main parameters on the evolutionary trends of the two sides is analyzed. The findings are as follows: (1) When both manufacturers and retailers undertake CSR, the retail price and wholesale price are their lowest, while carbon emission reduction, total market demand, manufacturer utility, retailer utility, and supply chain total utility are the highest. (2) When a company undertakes CSR, carbon emission reduction, total market demand, manufacturer utility, retailer utility, and supply chain total utility all increase with the increase in the CSR degree of the company and the ratio of the potential scale of low-carbon consumers to the potential scale of ordinary consumers. (3) The evolutionary stability strategy for both manufacturers and retailers is to undertake CSR. In addition, the initial proportion of manufacturers and retailers that undertake CSR, the low-carbon preference of low-carbon consumers, and the increase in the ratio of the potential scale of low-carbon consumers to the potential scale of ordinary consumers can encourage both members of the supply chain to undertake CSR.","PeriodicalId":36394,"journal":{"name":"Systems","volume":"36 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141887390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fayyaz Qureshi, Sarwar Khawaja, Katarina Sokić, Mirjana Pejić Bach, Maja Meško
In the realm of digital transformation, effective leadership and motivation are pivotal for organisations navigating the complexities of today’s systems. This study explores the intersection of intrinsic motivation and mental well-being among mature students—an analogy that sheds light on strategies applicable to organisational contexts. In developed nations like the UK, mental health for mature students is increasingly recognised as a crucial component of their educational journey. Mature students, who typically enrol in higher education after an educational gap and upon turning 21, often face specific challenges that can impact their mental well-being while pursuing academic goals. The primary objective of our study was to assess the relationship between intrinsic motivation and the mental well-being of mature students. The study included 248 full-time undergraduate mature students enrolled in private higher education institutions in the UK. These participants were 21 years and older. The research employed two measurement scales: the four-item Intrinsic Motivation Scale, adapted from Jaramillo, and the Warwick–Edinburgh Mental Well-Being Scale. Data collection utilised online Google Forms with multiple choice self-report formatted questions, and our analysis involved both descriptive and inferential statistics. Our research revealed a significant positive correlation between variables of intrinsic motivation and mental well-being. Confirmatory factor analysis (CFA) results confirmed the construct in the model. The results indicated that intrinsic motivation significantly predicts mental well-being among mature students (Cohen’s effect size value, f2 = 0.54). Recognising and addressing the unique difficulties individuals encounter and providing appropriate support can enhance their well-being and contribute to the overall success of the higher education community.
{"title":"Exploring Intrinsic Motivation and Mental Well-Being in Private Higher Educational Systems: A Cross-Sectional Study","authors":"Fayyaz Qureshi, Sarwar Khawaja, Katarina Sokić, Mirjana Pejić Bach, Maja Meško","doi":"10.3390/systems12080281","DOIUrl":"https://doi.org/10.3390/systems12080281","url":null,"abstract":"In the realm of digital transformation, effective leadership and motivation are pivotal for organisations navigating the complexities of today’s systems. This study explores the intersection of intrinsic motivation and mental well-being among mature students—an analogy that sheds light on strategies applicable to organisational contexts. In developed nations like the UK, mental health for mature students is increasingly recognised as a crucial component of their educational journey. Mature students, who typically enrol in higher education after an educational gap and upon turning 21, often face specific challenges that can impact their mental well-being while pursuing academic goals. The primary objective of our study was to assess the relationship between intrinsic motivation and the mental well-being of mature students. The study included 248 full-time undergraduate mature students enrolled in private higher education institutions in the UK. These participants were 21 years and older. The research employed two measurement scales: the four-item Intrinsic Motivation Scale, adapted from Jaramillo, and the Warwick–Edinburgh Mental Well-Being Scale. Data collection utilised online Google Forms with multiple choice self-report formatted questions, and our analysis involved both descriptive and inferential statistics. Our research revealed a significant positive correlation between variables of intrinsic motivation and mental well-being. Confirmatory factor analysis (CFA) results confirmed the construct in the model. The results indicated that intrinsic motivation significantly predicts mental well-being among mature students (Cohen’s effect size value, f2 = 0.54). Recognising and addressing the unique difficulties individuals encounter and providing appropriate support can enhance their well-being and contribute to the overall success of the higher education community.","PeriodicalId":36394,"journal":{"name":"Systems","volume":"133 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141886224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The paradigm shift that has spurred the fourth industrial revolution, in what is termed Industry 4.0, has ushered in the need to adopt digital technologies throughout the worldwide industrial base to support system design efforts. The adoption of digital technologies with a digital enterprise and the creation of cyber–physical systems are central tenets of Industry 4.0 and directly support profitable business models, improvements in efficiency, and ensure durable quality for the modern industrial base. However, the techniques for engineering systems require new, improved, digital life cycle process models if Industry 4.0—and the goals for its integrated systems—are to be realized. The development of a technique that improves the life cycles for systems within the digital enterprise is required. The 15288-SysML Grid described herein supports the Industry 4.0 paradigm and its associated digital enterprise. This is accomplished through (1) the application of a modern life cycle process model (i.e., the adapted diamond); (2) the utilization of international standards for systems; and (3) the adoption of the four fundamental aspects of system design supported by model-based systems engineering (MBSE) and the systems modeling language (SysML).
{"title":"Engineering Systems with Standards and Digital Models: Development of a 15288-SysML Grid","authors":"Kevin MacG. Adams, Irfan Ibrahim, Steven Krahn","doi":"10.3390/systems12080276","DOIUrl":"https://doi.org/10.3390/systems12080276","url":null,"abstract":"The paradigm shift that has spurred the fourth industrial revolution, in what is termed Industry 4.0, has ushered in the need to adopt digital technologies throughout the worldwide industrial base to support system design efforts. The adoption of digital technologies with a digital enterprise and the creation of cyber–physical systems are central tenets of Industry 4.0 and directly support profitable business models, improvements in efficiency, and ensure durable quality for the modern industrial base. However, the techniques for engineering systems require new, improved, digital life cycle process models if Industry 4.0—and the goals for its integrated systems—are to be realized. The development of a technique that improves the life cycles for systems within the digital enterprise is required. The 15288-SysML Grid described herein supports the Industry 4.0 paradigm and its associated digital enterprise. This is accomplished through (1) the application of a modern life cycle process model (i.e., the adapted diamond); (2) the utilization of international standards for systems; and (3) the adoption of the four fundamental aspects of system design supported by model-based systems engineering (MBSE) and the systems modeling language (SysML).","PeriodicalId":36394,"journal":{"name":"Systems","volume":"87 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141868471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Emergencies often lead to the impairment of infrastructure systems, including transportation systems. It is necessary to analyze the uncertainty and correlation of transportation network capacity caused by emergencies, aiming at the problems of emergency facilities’ location and matching in emergency contexts. This study introduces novel concepts, such as flow distribution betweenness centrality (FD-BC) and the transport capacity effect coefficient (TC-EC). Furthermore, we introduce the ellipsoidal uncertainty set to characterize uncertainties in transport capacity. We construct a multi-criteria decision-making (MCDM) model and a multi-strength elitist genetic algorithm (multi-SEGA) to ensure the lower limit of transport capacity between demand and emergency points while minimizing decision-making costs. By designing an uncertain scenario example, we analyze the effect of the perturbation ratio and the uncertainty level on the robust location model. The following results were drawn: (1) Indicators FD-BC and TC-EC effectively indicated the importance of each section in the emergency transportation network. (2) The optimal value of the model’s objective function changed more significantly as the perturbation ratio and uncertainty level increased. (3) After reaching a certain uncertainty level, the robust model with an ellipsoidal uncertainty set became more conservative than the robust model with a box uncertainty set, which lacked practical significance. The research results guarantee the robustness of the emergency support system in uncertain conditions.
{"title":"A Robust Optimization Model for Emergency Location Considering the Uncertainty and Correlation of Transportation Network Capacity","authors":"Baixu Jiang, Yan Song","doi":"10.3390/systems12080277","DOIUrl":"https://doi.org/10.3390/systems12080277","url":null,"abstract":"Emergencies often lead to the impairment of infrastructure systems, including transportation systems. It is necessary to analyze the uncertainty and correlation of transportation network capacity caused by emergencies, aiming at the problems of emergency facilities’ location and matching in emergency contexts. This study introduces novel concepts, such as flow distribution betweenness centrality (FD-BC) and the transport capacity effect coefficient (TC-EC). Furthermore, we introduce the ellipsoidal uncertainty set to characterize uncertainties in transport capacity. We construct a multi-criteria decision-making (MCDM) model and a multi-strength elitist genetic algorithm (multi-SEGA) to ensure the lower limit of transport capacity between demand and emergency points while minimizing decision-making costs. By designing an uncertain scenario example, we analyze the effect of the perturbation ratio and the uncertainty level on the robust location model. The following results were drawn: (1) Indicators FD-BC and TC-EC effectively indicated the importance of each section in the emergency transportation network. (2) The optimal value of the model’s objective function changed more significantly as the perturbation ratio and uncertainty level increased. (3) After reaching a certain uncertainty level, the robust model with an ellipsoidal uncertainty set became more conservative than the robust model with a box uncertainty set, which lacked practical significance. The research results guarantee the robustness of the emergency support system in uncertain conditions.","PeriodicalId":36394,"journal":{"name":"Systems","volume":"38 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141868476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Guohui Song, Yongbin Wang, Xiaosen Chen, Hongbin Hu, Fan Liu
Online news platforms have become users’ primary information sources. However, they focus on attracting users to click on the news and ignore whether the news triggers a sense of engagement, which could potentially reduce users’ participation in public events. Therefore, this study constructs four indicators by assessing user engagement to build an intelligent system to help platforms optimize their publishing strategies. First, this study defines user engagement evaluation as a classification task that divides user engagement into four indicators and proposes an extended LDA model based on user click–comment behavior (UCCB), using which the attractiveness of words in news headlines and content can be effectively represented. Second, this study proposes a deep user engagement evaluation (DUEE) model that integrates news attractiveness and multiple features in an attention-based deep neural network for user engagement evaluation. The DUEE model considers various elements that collectively determine the ability of the news to attract clicks and engagement. Third, the proposed model is compared with the baseline and state-of-the-art techniques, showing that it outperforms all existing methods. This study provides new research contributions and ideas for improving user engagement in online news evaluation.
{"title":"Evaluating User Engagement in Online News: A Deep Learning Approach Based on Attractiveness and Multiple Features","authors":"Guohui Song, Yongbin Wang, Xiaosen Chen, Hongbin Hu, Fan Liu","doi":"10.3390/systems12080274","DOIUrl":"https://doi.org/10.3390/systems12080274","url":null,"abstract":"Online news platforms have become users’ primary information sources. However, they focus on attracting users to click on the news and ignore whether the news triggers a sense of engagement, which could potentially reduce users’ participation in public events. Therefore, this study constructs four indicators by assessing user engagement to build an intelligent system to help platforms optimize their publishing strategies. First, this study defines user engagement evaluation as a classification task that divides user engagement into four indicators and proposes an extended LDA model based on user click–comment behavior (UCCB), using which the attractiveness of words in news headlines and content can be effectively represented. Second, this study proposes a deep user engagement evaluation (DUEE) model that integrates news attractiveness and multiple features in an attention-based deep neural network for user engagement evaluation. The DUEE model considers various elements that collectively determine the ability of the news to attract clicks and engagement. Third, the proposed model is compared with the baseline and state-of-the-art techniques, showing that it outperforms all existing methods. This study provides new research contributions and ideas for improving user engagement in online news evaluation.","PeriodicalId":36394,"journal":{"name":"Systems","volume":"191 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141872930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The synergistic interaction between emergency management departments and enterprises constitutes a fundamental mechanism for mitigating the risks of technological accidents caused by natural disasters (Natech). The efficacy of this collaborative approach is impacted by comprehensive risk analyses and the game between government and enterprise. Predicated on these premises, the evolutionary game analysis of government–enterprise collaboration in coping with Natech risk was carried out. Firstly, an evolutionary game model of government–enterprise collaboration in coping with Natech risk was constructed. Secondly, the evolutionary stability strategy (ESS) was developed. Finally, these strategies were substantiated through numerical simulations. The findings revealed that at lower levels of Natech risk, enterprises will choose low investment in coping capacity building, and emergency management departments will choose delayed disposal. Under moderate Natech risk, enterprises will increase their investments if emergency management departments persist with delayed strategies, and emergency management departments will react proactively if enterprises persist with low investment. Under a high Natech risk, a pattern of strategic misalignment emerges between the two entities. This study contributes a theoretical basis for the optimization of government–enterprise collaboration in coping with Natech risks.
{"title":"Evolutionary Game Analysis of Government–Enterprise Collaboration in Coping with Natech Risks","authors":"Shaojun Guo, Wei Feng, Guirong Zhang, Ying Wen","doi":"10.3390/systems12080275","DOIUrl":"https://doi.org/10.3390/systems12080275","url":null,"abstract":"The synergistic interaction between emergency management departments and enterprises constitutes a fundamental mechanism for mitigating the risks of technological accidents caused by natural disasters (Natech). The efficacy of this collaborative approach is impacted by comprehensive risk analyses and the game between government and enterprise. Predicated on these premises, the evolutionary game analysis of government–enterprise collaboration in coping with Natech risk was carried out. Firstly, an evolutionary game model of government–enterprise collaboration in coping with Natech risk was constructed. Secondly, the evolutionary stability strategy (ESS) was developed. Finally, these strategies were substantiated through numerical simulations. The findings revealed that at lower levels of Natech risk, enterprises will choose low investment in coping capacity building, and emergency management departments will choose delayed disposal. Under moderate Natech risk, enterprises will increase their investments if emergency management departments persist with delayed strategies, and emergency management departments will react proactively if enterprises persist with low investment. Under a high Natech risk, a pattern of strategic misalignment emerges between the two entities. This study contributes a theoretical basis for the optimization of government–enterprise collaboration in coping with Natech risks.","PeriodicalId":36394,"journal":{"name":"Systems","volume":"151 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141868477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this study, a tripartite decision-making parking pricing model was developed based on Game Theory to comprehensively reflect the impact of parking pricing on private car travelers, parking lot operators, and traffic managers. Utility theory is introduced to analyze the behavioral characteristics of the tripartite participants in parking pricing. A parking behavior model for private car travelers, an operating profit model for parking lot operators, and a social negative utility model for traffic managers are established. This article presents an analysis of the mutual influence between them based on a game theory perspective and introduces parking saturation and road saturation as new factors influencing parking pricing to address the interactive relationship among the tripartite participants. A parking pricing model based on tripartite games is established, and a solution algorithm is designed. The results indicate that when the parking fee rates for the two public parking lots in the scenario are 8.5 CNY/h and 9 CNY/h, respectively, the parking demand is 300, and the sum of the total travel costs of private car travelers and the total operating profits are CNY 20,589 and 2187.9, respectively. The parking saturation of the public parking lot and the difference between the expected value is minimized to 0.25, and the road saturation and the difference between the expected value are minimized to 1.48, which is the parking pricing plan that minimizes the conflicts of interest among the tripartite stakeholders in the tripartite game. The parking pricing model of a public parking lot provides a reference for formulating parking fee strategies that comprehensively reflect the needs of the three parties involved in the public parking lot.
{"title":"A Pricing Model Study of Shared Parking Area Charge Based on Game Theory","authors":"Chao Sun, Haodong Jing, Haowei Yin","doi":"10.3390/systems12080269","DOIUrl":"https://doi.org/10.3390/systems12080269","url":null,"abstract":"In this study, a tripartite decision-making parking pricing model was developed based on Game Theory to comprehensively reflect the impact of parking pricing on private car travelers, parking lot operators, and traffic managers. Utility theory is introduced to analyze the behavioral characteristics of the tripartite participants in parking pricing. A parking behavior model for private car travelers, an operating profit model for parking lot operators, and a social negative utility model for traffic managers are established. This article presents an analysis of the mutual influence between them based on a game theory perspective and introduces parking saturation and road saturation as new factors influencing parking pricing to address the interactive relationship among the tripartite participants. A parking pricing model based on tripartite games is established, and a solution algorithm is designed. The results indicate that when the parking fee rates for the two public parking lots in the scenario are 8.5 CNY/h and 9 CNY/h, respectively, the parking demand is 300, and the sum of the total travel costs of private car travelers and the total operating profits are CNY 20,589 and 2187.9, respectively. The parking saturation of the public parking lot and the difference between the expected value is minimized to 0.25, and the road saturation and the difference between the expected value are minimized to 1.48, which is the parking pricing plan that minimizes the conflicts of interest among the tripartite stakeholders in the tripartite game. The parking pricing model of a public parking lot provides a reference for formulating parking fee strategies that comprehensively reflect the needs of the three parties involved in the public parking lot.","PeriodicalId":36394,"journal":{"name":"Systems","volume":"245 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141777719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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