Network Analysis Model Research Articles

Transcriptome Sequencing-Based Screening of Key Melatonin-Related Genes in Ischemic Stroke

Ischemic stroke (IS) is a complex syndrome of neurological deficits due to stenosis or occlusion of the carotid and vertebral arteries for which there is still no effective treatment. Melatonin, a hormone secreted by the pineal gland, has multiple biological effects, such as antioxidant and anti-inflammatory properties, circadian rhythm regulation, and tissue regeneration, demonstrating potential applications in the treatment of IS. The aim of this study was to investigate key melatonin-regulated genes associated with IS using transcriptome sequencing and bioinformatics analyses and to explore their potential mechanisms of action in the disease process. We obtained gene expression data related to ischemic stroke (IS) from the Gene Expression Omnibus (GEO) database and identified candidate genes using machine learning algorithms. We then assessed the predictive power of these genes using PPI network analysis and diagnostic models. Finally, a series of enrichment analyses identified four key genes: ADM, PTGS2, MMP9, and VCAN. In addition, we determined the mRNA levels of these four key genes in an IS rat model using qPCR and found that all of these genes were significantly upregulated in the IS model compared to the control group, which is consistent with the results of previous analyses. Meanwhile, these genes have biological functions such as regulating vascular tone, participating in the inflammatory response, influencing tissue remodeling, and regulating cell adhesion and proliferation, playing key roles in the pathogenesis of IS. Therefore, we suggest that these four key genes may serve as prospective biomarkers for IS and help predict the risk of developing IS. In conclusion, this study elucidates for the first time the potential role of melatonin in the pathogenesis of IS and lays the foundation for in-depth studies on the functions of these key genes in the pathophysiology of IS and their potential applications in clinical diagnosis and treatment.

뉴스 기사 빅데이터 분석을 통한 북한이탈주민의 사회적 이슈와 언어 문제 탐색: 2019-2024

Objectives The purpose of this study is to explore social issues and language problems that appear in news articles related to North Korean defectors and to suggest their implications. Methods 561 news articles related to North Korean defectors covered by major domestic newspapers and media companies from 2019 to June 2024 were collected. For analysis tools, language network analysis and topic modeling techniques were used to analyze major keywords and topics and visualize them. In addition, based on the analysis results, social issues and language problems of North Korean defectors were explored, and Korean language education implications were presented. Results he research results are as follows. First, the amount of news coverage has been increasing significantly over the past three years, and political articles have shown an overwhelmingly high proportion. Second, the main keywords are ‘government, support, human rights, protection, and settlement’. The main topics were protection of human rights of North Korean defectors, increasing number of North Korean defectors among elites and the 2030 generation, and complaints of difficulties due to mental and economic discrimination and prejudice in the process of settling in Korea. The content of the government's integrated support policy accounted for a high proportion. Third, it was found that North Korean defectors were struggling with the language difference between North and South Korea, and Korean language education focusing on pronunciation, accent, and vocabulary in each area was necessary for them. Conclusions The main topic of the news article was the integrated support for the protection of human rights and stable settlement of North Korean defectors. However, although North Korean defectors complain of ‘difficulty’ due to economic, psychological, emotional, and linguistic differences, these related topics are hardly covered in the media. Therefore, this paper suggested implications for Korean language education research for North Korean defectors' language adaptation.

Crisis management from a relational perspective: an analysis of interorganizational transboundary crisis networks

Abstract Although transboundary crises have gained relevance in an increasingly interdependent world, our understanding of the relational dynamics governing these phenomena remains limited. This paper addresses this knowledge gap by identifying common characteristics across interorganizational transboundary crisis networks and drivers of tie formation in successful structures. For this purpose, it applies descriptive Social Network Analysis and Exponential Random Graph Models to an original dataset of three networks. Results show that these structures combine elements of issue networks and policy communities. Common features include moderately high centralization, reciprocated ties, core-periphery structures, and the popularity of international organizations. Additionally, successful networks display smooth communication between NGOs and international organizations, whereas unsuccessful networks have fewer heterophilous interactions. Transitivity seems to play a role in network success too. These findings suggest that crisis networks are robust structures that reconcile bridging and bonding dynamics, thereby highlighting how evidence from relational studies could guide transboundary crisis management.

A Brain Network Analysis Model for Motion Sickness in Electric Vehicles Based on EEG and fNIRS Signal Fusion

Motion sickness is a common issue in electric vehicles, significantly impacting passenger comfort. This study aims to develop a functional brain network analysis model by integrating electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals to evaluate motion sickness symptoms. During real-world testing with the Feifan F7 series of new energy-electric vehicles from SAIC Motor Corp, data were collected from 32 participants. The EEG signals were divided into four frequency bands: delta-range, theta-range, alpha-range, and beta-range, and brain oxygenation variation was calculated from the fNIRS signals. Functional connectivity between brain regions was measured to construct functional brain network models for motion sickness analysis. A motion sickness detection model was developed using a graph convolutional network (GCN) to integrate EEG and fNIRS data. Our results show significant differences in brain functional connectivity between participants in motion and non-motion sickness states. The model that combined fNIRS data with high-frequency EEG signals achieved the best performance, improving the F1 score by 11.4% compared to using EEG data alone and by 8.2% compared to using fNIRS data alone. These results highlight the effectiveness of integrating EEG and fNIRS signals using GCN for motion sickness detection. They demonstrate the model’s superiority over single-modality approaches, showcasing its potential for real-world applications in electric vehicles.

Relationships between mental toughness, eustress-distress, and mindfulness in adolescents: A network analysis and mediator model testing.

The main aim of this study was to investigate adolescents' eustress-distress and mindfulness by examining mental toughness (MT) using network and mediator analyses. The study included 414 adolescents. The results showed that MT was positively related to eustress and mindfulness, but distress was negatively related to MT. Based on the network analysis findings, we tested mindfulness' mediating role in the relationship between eustress-distress and MT. The findings showed that mindfulness played a significant mediating role. However, the mediating role of mindfulness was negative for the relationship between distress and toughness and positive for eustress. These findings advance eustress, distress, and mindfulness as mechanisms for understanding the effects of MT. Considering current knowledge of MT, eustress, distress, and mindfulness, the results are discussed.

Key factors influencing sustainable population growth: A DEMATEL-ANP combined approach

This study highlights the importance of sustainable social demographic growth. It uses a model that combines the Decision-making Trial and Evaluation Laboratory (DEMATEL) with the Analytic Network Process (ANP) to examine key factors affecting this growth and their interactions. The analysis focuses on six critical factors: economic development, education and gender equality, health services, environmental sustainability, immigration policies, and technological advancement. Experiments using government and international organization databases include comparative experiments with deep learning prediction models, ensemble learning models, Causal Inference Models, complex network analysis models, and agent-based models. Comparison metrics cover accuracy, precision, recall, and F1 score. The results indicate that, with a data volume of 4000, the optimized model achieves an accuracy of 0.973, precision of 0.981, recall of 0.969, and an F1 score of 0.89, demonstrating the model's superior performance. The DEMATEL method analyzes the direct relationships among the factors. The results show that economic development and technological advancement have impact scores of 3.91 and 3.43, respectively, indicating their strong influence on other factors and their role in promoting sustainable demographic growth. Education and gender equality, health services, and technological advancement each have impact scores of 3.39, meaning they are significantly affected by other factors and are sensitive in the growth process. Finally, the ANP method is used to calculate the weights of each factor, determining their relative importance in sustainable social demographic growth. The results highlight that economic development level and technological advancement and innovation are core factors influencing sustainable social demographic growth, with significant direct and indirect impacts on other factors and a crucial role in the overall system. These findings provide a scientific basis for formulating relevant policies and interventions, particularly in prioritizing and strengthening economic and technological development strategies. This study offers valuable insights for research in demography, sustainable development, and social policy formulation.

글 없는 그림책 연구 동향 분석 : 키워드 네트워크 분석 및 토픽모델링을 중심으로

This study explores research trends in wordless picture books using text mining techniques. For this purpose, abstracts of 181 articles were collected, and then data preprocessing and analysis were performed. In the data analysis process, keyword network analysis and topic modeling analysis were performed. The keyword analysis showed that the most frequent word was ''young children'', followed by ''storytelling'' and ''wordless picture book activities''. The network analysis showed that the strength of the connections between ''wordless picture book activities → young children'' and ''using wordless picture books → young children'' was high. This suggests that in studies of wordless picture books, the use of picture book activities or wordless picture books is mainly targeted at young children. Topic modeling resulted in four themes: multicultural approach and visual literacy, playfulness and artistic development, integration of language and art, and methodology of integration and repetition. This study is significant in that it identifies major research themes in the field of wordless picture books and suggests future research directions.

Centrality and bridge connections between cognitive emotion regulation strategies and professional identity among Chinese undergraduate nursing students: A network analysis

AimTo construct a network structure for cognitive emotion regulation among Chinese undergraduate nursing students and identify central nodes and to explore the bridge connections between cognitive emotion regulation strategies and professional identity from the perspective of network analysis. BackgroundUndergraduate nursing students are currently in a poor psychological condition and cognitive emotion regulation strategies can help them use positive approaches to regulate their emotions. There may be a link between cognitive emotion regulation strategies and professional identity. DesignThis was a cross-sectional study that used network analysis. MethodA total of 218 Chinese undergraduate nursing students were selected and surveyed using the Cognitive Emotion Regulation Questionnaire and the Professional Identity Questionnaire. A network analysis model was constructed and the related indices were calculated using R 4.3.0 software. ResultsNetwork analysis showed that the central nodes of undergraduate nursing students’ cognitive emotion regulation strategies were positive reappraisal, refocusing on planning and catastrophising; In the bridge network of cognitive emotion strategies and professional identity, professional self-concept, positive reappraisal, benefits of stay and risk of resignation and refocusing on planning were the nodes with the strongest bridge strength. ConclusionThe salient central and bridge nodes can serve as potential targets for interventions aimed at improving the mental health of undergraduate nursing students. Nursing educators must be trained in cognitive emotion regulation and appropriately guided to use positive emotion regulation strategies in their studies and work. Educators should focus on the relation between cognitive emotion regulation and professional identity to improve the mental health of nursing students and stabilise the nursing workforce.

A Study on Internet News for Patient Safety Campaigns: Focusing on Text Network Analysis and Topic Modeling.

Background/Objectives: This study aimed to identify the main issues related to public patient safety campaigns reflected in Korean online news. This study utilized a text-mining method to identify keywords and topics related to patient safety campaigns. Methods: The data collection period was from 1 January 2022 to 31 December 2023, and 4110 news articles were extracted. Through data preprocessing, 2661 duplicated news and 1213 unrelated news were removed, and 236 news were selected. Using the NetMiner program, keyword co-occurrence frequency calculation, keyword centrality analysis, and topic modeling analysis were performed. Results: The results showed that the most frequently mentioned keywords with high degree centrality, betweenness centrality, and closeness centrality in online news were "hospital", "medical", "medicine", "project", and "treatment". The topics of online news related to the patient safety campaign were "patient-centered care for medical safety", "health promotion projects at a regional institution", "hand hygiene education to prevent infection", "healthcare quality improvement through the Mint Festival", and "safe use of medicines". Conclusions: This study analyzed patient safety campaign news topics using text network analysis and topic modeling. It was confirmed that patient safety campaigns are essential for fostering a patient safety culture, improving medical quality, and encouraging patient participation in hospitals. Therefore, to build a safe medical environment, it is necessary to establish an effective patient safety campaign for not only medical staff providing medical care, but also patients and their caregivers, and for this, cooperation and participation from various professional occupations are necessary.

A conceptual framework for exploring the use of a green marketing mix and brand image to attract customers for green services while visiting a hotel: A step toward marketing sustainability

Recently, the application of green marketing strategies to promote marketing sustainability has increased in response to the growing demand for eco-friendly products, Price, promotion, place, and service are all variables that influence purchasing decisions, and the current study aims to confirm this. as the brand plays a significant role as well because it is regarded as the driving force behind consumer attraction in society. For this study, a framework connecting brand image (brand benefits and competence, brand identity, brand personality, brand association, brand attitude, and behavior), the green marketing mix (green services, green place, green price, green promotion), and green purchasing decisions were created to better understand the relationships among these variables. Artificial neural network (ANN) analysis and structural equation modeling (SEM) based on deep learning will be conducted via the new hybrid analysis approach. The research employed a survey of hotel customers in Iraq, Additionally, the researchers conducted structured interviews with a group of hotel managers. The issue of green marketing and how it relates to environmentally conscious consumer choices has received much attention, which has made addressing sustainability a top priority in the hospitality industry, particularly in hotels. In this study, customers were the main focus since their choice is crucial regarding switching to green services. However, a group of these hotel managers were interviewed to validate the findings and establish credibility. The current study was limited since it focused on clients in general, without considering clients from outside Iraq. Future studies can examine the variable of organizational support and the degree of change acceptance among customers.

Investigating the behaviors of core and periphery students in an asynchronous online discussion community using network analysis and topic modeling

Investigating the behaviors of core and periphery students in an asynchronous online discussion community using network analysis and topic modeling

Targeting ADA and HIV-1 Nef Protein with Protease Inhibitors: A Repurposing Strategy through Molecular Networking and an In silico Approach for Integrated Management of HIV Co-infected with Abdominal Tuberculosis

Background: Background: The co-infection of HIV and abdominal TB poses a worldwide danger to humanity. This is because there are more strains of bacteria that are resistant to many classes of currently available medications. According to current findings, repurposing existing available medications will result in more effective functioning than using newly designed medications. Objective: Based on this fact, we hypothesised that the PI could be repurposed; we used Food and Drug Administration (FDA)-approved PI drugs to treat HIV co-infected patients with abdominal TB, and a computational study has been conducted. Methods: This comprises network analysis models to find their protein drug interaction (PDI) through a search tool for interacting chemicals (STICH) module of Cytoscape network analysis model followed by the screening of these drugs for their ADMET prediction and binding affinity with adenosine deaminase (ADA), a protein responsible for abdominal TB, and the HIV-1 Nef protein, responsible for the regulation of immune function (CD4+). Results: The network analysis showed 13 nearest binding drugs of these proteins of interest. The ADMET study result showed the pharmacologically relevant parameters that have a significant effect on the binding affinity, bioavailability, and toxicity of PI. The top three scores achieved by PI against adenosine deaminase enzyme activity (PDB ID: 1A4M) are viz., -23.7919, -23.3529, and -22.6773 for Ritonavir, Tipranavir, and Atazanavir, respectively. The top three scores achieved by PI against HIV-1 Nef protein activity (PDB ID: 6URI) are viz., -28.7321, -28.4987, and -28.3155 for Atazanavir, Tipranavir, and Simeprevir, respectively. The active site of ADA and HIV-1 Nef proteins comprises amino acid residues such as for Tipranavir: Arene-Cation interaction (Phenyl and Pyridine)- Arg B1081, and Lys B1033 (1A4M) and Arene-Cation interaction (Pyridine and Phenyl)- Lys D11 and Arg D33; Sidechain acceptor Thr B40; Sidechain donor- Asp D30; Backbone donor- Ala B37 (6URI). Atazanavir: Arene-Cation interaction (Phenyl)- Lys A254 and Lys B1033; Sidechain acceptor - Arg A251 (1A4M). Conclusion: Thus, from the computational studies carried out, we could obtain hints for optimising the molecular selectivity of the PI to provide help in the design of new compounds via the repurposing strategy of the FDA-approved PI for effective treatment of co-morbidity with HIV and abdominal TB. However, further pharmacokinetics, pharmacodynamics, preclinical, and clinical studies permit the design of the new agents without undesirable interactions.

Understanding the rationales and information environments for early, late, and nonadopters of the COVID-19 vaccine

Anti-vaccine sentiment during the COVID-19 pandemic grew at an alarming rate, leaving much to understand about the relationship between people’s vaccination status and the information they were exposed to. This study investigated the relationship between vaccine behavior, decision rationales, and information exposure on social media over time. Using a cohort study that consisted of a nationally representative survey of American adults, three subpopulations (early adopters, late adopters, and nonadopters) were analyzed through a combination of statistical analysis, network analysis, and semi-supervised topic modeling. The main reasons Americans reported choosing to get vaccinated were safety and health. However, work requirements and travel were more important for late adopters than early adopters (95% CI on OR of [0.121, 0.453]). While late adopters’ and nonadopters’ primary reason for not getting vaccinated was it being too early, late adopters also mentioned safety issues more often and nonadopters mentioned government distrust (95% CI on OR of [0.125, 0.763]). Among those who shared Twitter/X accounts, early adopters and nonadopters followed a larger fraction of highly partisan political accounts compared to late adopters, and late adopters were exposed to more neutral and pro-vaccine messaging than nonadopters. Together, these findings suggest that the decision-making process and the information environments of these subpopulations have notable differences, and any online vaccination campaigns need to consider these differences when attempting to provide accurate vaccine information to all three subpopulations.

Fuzzy deep visual panic detection

Deep visual data analysis from social network has become an increasingly important area of research. In fact, this form of assessment makes it viable to recognize new information on social users which incorporates emotions. In order to recognize users’ emotions and other latent attributes, most of the existing approaches have used textual data and have obtained accurate results. However, little attention has been paid to visual data that have become increasingly popular in recent years.This work describes how to develop a conceptual representation model for social network analysis and social emotion analysis based on the integration of fuzzy logic and ontological modeling. The primary aim is to create an ontology that can extract new information about a social user’s mood, which can be classified as panic, no-panic, or neutral. Fuzzy logic is necessary to deal with subjective data, as users share imprecise and vague data on their social profiles. Fuzzy logic has been observed as a successful method to capture the expression of emotions due to the fuzzy nature of emotions and the ambiguous definitions of emotion words. The proposed work investigate the role of fuzzy logic in social network analysis. This study simulate a fuzzy deep system integrated with ontology for classifying social visual data (shared images) into panic, no-panic or neutral classes in order to determine the social users’ stress intensity. The Social distancing and the huge amount of shared data in Tunisia were calculated to demonstrate this classification. The experiments performed in this paper aim to create not only a novel annotated visual database named visual panic database, but also a new semantic model for modeling users’ profiles, in social network, based on the combination between ontology and deep learning techniques. In this context, we will work in the future on the combination between a user’s visual and textual data in order to improve the performance of this recognition. The proposed fuzzy system reflected the viral proliferation in stressed users and achieved an accuracy of 87%.

A study on the spatial correlation network structure and its influencing factors of coupling coordination between FDI flow network and carbon transfer network in the belt and road initiative countries

Clarifying the spatial correlation characteristics and influencing factors of coupling coordination between Foreign Direct Investment (FDI) flow networks and carbon transfer networks in countries along the Belt and Road Initiative is of utmost importance for the formulation of regional carbon governance strategies and the establishment of a high-quality Green Silk Road. This study used a comprehensive approach combining social network analysis and coupling coordination model to measure the coupling coordination degree of FDI flow networks and carbon transfer networks of 67 Belt and Road countries from 2010 to 2016. In addition, a modified gravity model is used to characterize the spatial correlation network structure of coupling coordination between the two networks, and the QAP regression analysis method is applied to investigate the factors influencing the spatial association network. The results are as follows: 1) The spatial correlation network of coupling coordination between the two networks has good accessibility and relatively high overall network stability. 2) Countries such as Qatar and the United Arab Emirates occupy central positions in the network, while Bahrain and Jordan are positioned on the periphery of the network. 3) The spatial correlation network can be divided into three sectors: net outflow, net inflow, and bidirectional overflow sectors. 4) Spatial adjacency, bilateral investment treaties, economic development, and institutional quality have significant positive effects on the spatial association network, while the industrial structure and the level of infrastructure development have a significant negative impact. This study proposes an indicator system for the coupling coordination between FDI flow networks and carbon transfer networks. The aim is to investigate the coupling coordination relationship between FDI flow networks and carbon emission transfer networks in countries along the Belt and Road Initiative, providing important guidance for the formulation of regional cooperative carbon emission reduction strategies in other regions.

Heterogeneous palaeo‐ecogeography of brachiopods during the Late Ordovician mass extinction in South China

AbstractFollowing the first phase of the Late Ordovician mass extinction (LOME), the globally distributed Hirnantia brachiopod fauna exhibited homogeneity both latitudinally and longitudinally. The uniform global palaeobiogeography of the Hirnantia fauna, based solely on occurrence data, might obscure its heterogeneous nature during the glaciation. In this study, leveraging diversity and abundance data from well‐sampled collections in South China, along with non‐metric multidimensional scaling (NMDS) using the Raup–Crick measure, network analysis (NA), and abundance models, we unveil the palaeo‐ecogeography of the Hirnantia fauna for the first time. The distribution of the Hirnantia fauna in South China is categorized into three areas: the deep water area, the shallower water area, and the very shallow water area, based on NMDS and NA. Drastically different benthic assemblages among these areas reveal diverse environmental settings, with an approximate westward shallower trend and a deeper trend extending both northward and southeastward. Analysis of species‐abundance models for eight well‐sampled collections demonstrates different best‐fitted models, suggesting that competitive ecology operated in varied contexts despite common BA3 environmental settings and close geographical proximity. While a substantial global sea level fall and climate cooling around the Katian–Hirnantian boundary played important roles in the LOME, the palaeo‐ecogeography of the Hirnantia fauna in South China was predominantly influenced by the expansion of the Cathaysian oldland and the Qianzhong uplift. The interval between the two LOME phases, marked by kinetic conditions, witnessed the heterogeneous development of the Hirnantia fauna.

A Review of Traffic Flow Prediction Models in 5G Using Machine Learning Techniques

Abstract: Wireless technology has advanced significantly since its origin and is now an essential aspect of our daily life. The advancement of wireless communication from the first generation (1G) to the fifth generation (5G) of technology has been revolutionary. An extensive summary of the development of wireless communication from 1G to 5G has been given in this paper. Cellular networks are heading towards becoming more diverse, broadband, integrated, and intelligent networks with the introduction of 5G networks. The goals of 5G wireless technology are to provide more users with more consistent user experiences, ultralow latency, vast network capacity, faster multi-Gbps peak data speeds and increased reliability. While the resources needed for computation and communication are also growing with the maturity of 5G technology .At the same time, cellular traffic has increased dramatically due to the widespread use of smart devices. Cellular traffic prediction is a crucial component of the resource management system for cellular networks but it confronts many difficulties due to strict standards for accuracy and dependability. Among the most important issues is how to enhance the predictive performance of Mobile data traffic. This review describes the need of traffic forecasting in cellular network in 5G technology. A study of different models for network analysis and traffic prediction by different researchers is presented in this paper. The distinctiveness and guidelines of earlier research for traffic prediction in 5G are examined. To determine the distinctive qualities of each method used for traffic prediction in mobile network, a thorough analysis of the most popular techniques using Machine learning for predictive analysis are discussed.

Comparison of the coexistence pattern of mangrove macrobenthos between natural and artificial reforestation.

The abandoned pond-to-mangrove restoration project provides greater advantages than tidal flats afforestation in restoring mangrove ecosystem services and will be the primary method for mangrove restoration in the future. The existing methods for abandoned pond-to-mangrove restoration include artificial restoration through 'dike-breaking, filling with imported soil and tree planting' and natural restoration through 'dike-breaking and natural succession'. However, little is known about which restoration strategy (natural or artificial restoration) provides more benefits to the biodiversity of mangrove macrobethos. Given a prevailing view suggested that artificial restoration should be the preferred approach for accelerating recovery of biodiversity and vegetation structure in tropical regions, we hypothesised higher macrobenthic biodiversity and more complex community structure in artificial restoration than in natural restoration. To test this hypothesis, macrobenthic biodiversity and ecological processes were monitored in a typical abandoned pond-to-mangrove area of Dongzhaigang Bay, China, where artificial and natural restoration methods were used concurrently. Differences in macrobenthic biodiversity, community structure and ecological processes were compared using diversity indices, complex network analysis and null models. Similar species composition and ecological niche overlap and width among macrobenthos were observed at artificial and natural restoration sites. The biotic heterogeneity and interaction among macrobenthos were higher at the natural restoration sites than at the artificial restoration sites. Macrobenthos community assembly at natural and artificial restoration sites was both determined by deterministic processes, with environmental filtering dominating, which explained 52% and 54% of the variations in macrobenthic community structures respectively. Although our findings did not validate the research hypothesis, higher biotic heterogeneity and species interaction among macrobenthos could support natural restoration as the primary method for abandoned pond-to-mangrove projects, because it is a nature-based solution for mangrove restoration.

The driving mechanisms of industrial air pollution spatial correlation networks: A case study of 168 Chinese cities

To enhance regional collaborative management and effectively address air pollution, this paper constructs the industrial air pollution network of 168 cities in China between 2010 and 2021. Social network analysis (SNA) and temporal exponential random graph model (TERGM) are used to investigate the structural characteristics and driving mechanisms of industrial air pollution spatial correlation networks. The results indicate the following: (1) Industrial air pollution levels in key Chinese cities showed a slight fluctuation between 2010 and 2015, but decreased after 2015. (2) The industrial air pollution linkage network exhibits small-world characteristics and path dependence, with different cities playing distinct roles in the network. (3) The evolution of the network is influenced by both exogenous and endogenous mechanisms. Local network structures such as reciprocity, connectivity, and transitivity make the network exhibit path-dependent characteristics. Cities’ attributes such as economic development level, green innovation capacity, and population density significantly influenced their status in the network and linkages between cities. Pollution linkages are more likely to occur in cities with similar levels of economic development, green technological innovation capacity, and institutional environments. Therefore, effective regional division for scientific collaborative governance should be considered from multiple perspectives. Based on these results, the paper provides several policy recommendations.

Towards a Sustainable Structure of an Urban Water–Energy–Food Nexus: Based on Network and Hierarchy Analysis

Water, energy, and food (WEF) are critical resources to sustain urban development, which requires a sustainable structure of the urban WEF nexus to address trade-offs and achieve synergies. Although interactions in the WEF nexus are widely explored, its sustainable structure has largely been ignored. This study constructs a framework of WEF nexus sustainability factors. Based on a literature review and expert opinions, 21 factors influencing urban WEF nexus sustainability were extracted and their interrelationships determined. We used social network analysis (SNA) and interpretive structural modeling (ISM) to analyze the structure of the urban WEF network. The results indicate that technological investment and industrial added-value energy consumption are the most critical and fundamental factors for promoting the sustainable development of the urban WEF nexus. Additionally, the balance of water supply and demand and the comprehensive management of waste pollutants are also driving and supporting factors for the sustainability of the urban WEF nexus. The results of this study complement the interaction mechanism research of the urban WEF nexus and provide practical references for sustainable decision-making in urban WEF nexus practices.