A study of the spatial network structure of ethnic regions in Northwest China based on multiple factor flows in the context of COVID-19: Evidence from Ningxia

Maternal life events, anxiety, social support, and prenatal depression among pregnant women: a cross-sectional study in Yinchuan, China

While the association between life events and depression has been well-established, the mechanisms linking life events to depression in pregnant women require further exploration. This study aimed to explore the association between maternal life events and depression, with anxiety and social support as potential influencing factors. A cross-sectional study was conducted from January 2020 to December 2021 among 1767 pregnant women recruited via convenience sampling from a tertiary hospital in Yinchuan, China. Participants completed the Patient Health Questionnaire-9 (PHQ-9), Life Events Scale for Pregnant Women (LESPW), Generalized Anxiety Disorder scale (GAD-7), and Social Support Rating Scale (SSRS). Data were analyzed using SPSS 26.0 and PROCESS 3.5. The key findings revealed that: maternal life events were positively associated with depression, with a total effect of 0.323 (95%CI = 0.283-0.363, p < 0.001) and a direct effect of 0.117 (95%CI = 0.084-0.151, p < 0.001). Anxiety partially mediated this association, accounting for 63.8% of the total effect (indirect effect = 0.206, 95%CI = 0.175-0.239, p < 0.001). Social support and its three dimensions negatively influenced the association between maternal life events and anxiety(moderating effects: overall social support β=-0.840, subjective support β=-0.786, objective support β=-0.360, support utilization β=-0.441, all p < 0.01). These findings suggest that maternal life events influence prenatal depression both directly and indirectly, while social support mitigates anxiety triggered by stressors. Targeted interventions to reduce life event exposure and strengthen social support may alleviate anxiety and depression in pregnant women.

Spatial structure and organization of the medical device industry urban network in China: evidence from specialized, refined, distinctive, and innovative firms

Introduction

Investigating the network of firms in a specific industry helps explain industrial location and urban functions and provides guidelines for promoting industrial restructuring and high-quality development.

Methods

This study develops a network model for the relationship between firms and cities based on the data of listed Specialized, Refined, Distinctive, and Innovative (SRDI) medical device manufacturing firms in China to identify the spatial distribution and influencing factors of the urban network of such firms using network analysis and GeoDetector.

Results and disscusion

Three conclusions are obtained from the study. First, the urban network of listed SRDI medical device manufacturing firms exhibits a sparse structure, with the density decreasing from east to west, and the out-degree presenting significant spatial concentration. Suzhou, Shanghai, and Shenzhen are the core of the network power. The in-degree presents low spatial concentration. Clearly differentiated network functions are observed. Second, significant spatial differences are noted between high- and low-level linkage networks from the perspective of corporate governance structure. Third, economic level, labour costs, level of opening-up, talent base, and technological innovation capability have significant effects on the urban network of listed SRDI medical device manufacturing firms.

Analysis and early warning management of resource and environmental carrying capacity in agricultural provinces: A case study of Henan Province

Resources and Environmental Carrying Capacity (RECC) is a comprehensive concept that encompasses the interactions between resources, the environment, and human activities, serving as the foundation for social development strategies. To adequately reflect this complex relationship, a multi-level, multi-dimensional evaluation indicator system must be developed. This paper constructs a regional soil environmental evaluation system incorporating PM2.5 indicators, which is in line with relevant environmental protection policies and planning orientations in our country from 2014 to 2023. It analyzes the level and development trend of RECC in Henan Province and proposes measures for effective management. The results indicate the following: (1) The RECC in Henan Province demonstrates a downward trajectory, marked by temporary fluctuations over time. It hit its nadir in 2019, subsequently undergoing a gradual resurgence; (2) Analysis of individual dimension indicators reveals that the natural carrying capacity has declined from a medium to a relatively weaker level. Meanwhile, environmental carrying capacity has shown a slight downward trend but has generally remained stable. In contrast, socio-economic carrying capacity has demonstrated an upward trend, rising from a medium to a relatively strong level. In terms of early warning measures, it is essential to establish a red warning zone, implement a credit record accountability system, and develop a monitoring warning database along with an information technology platform. This paper demonstrates that the indicator system is effective for evaluating RECC across different dimensions and holds significant reference value for assessing RECC in similar regions.

A cross-sectional study of parental perspectives on children about COVID-19 and classification using machine learning models

Background and objective

This study delves into the parenting cognition perspectives on COVID-19 in children, exploring symptoms, transmission modes, and protective measures. It aims to correlate these perspectives with sociodemographic factors and employ advanced machine-learning techniques for comprehensive analysis.

Method

Data collection involved a semi-structured questionnaire covering parental knowledge and attitude on COVID-19 symptoms, transmission, protective measures, and government satisfaction. The analysis utilised the Generalised Linear Regression Model (GLM), K-Nearest Neighbours (KNN), Support Vector Machine (SVM), Random Forest (RF), Naive Bayes (NB), and AdaBoost (AB).

Results

The study revealed an average knowledge score of 18.02 ± 2.9, with 43.2 and 52.9% of parents demonstrating excellent and good knowledge, respectively. News channels (85%) emerged as the primary information source. Commonly reported symptoms included cough (96.47%) and fever (95.6%). GLM analysis indicated lower awareness in rural areas (β = -0.137, p < 0.001), lower attitude scores in males compared to females (β = -0.64, p = 0.025), and a correlation between lower socioeconomic status and attitude scores (β = -0.048, p = 0.009). The SVM classifier achieved the highest performance (66.70%) in classification tasks.

Conclusion

This study offers valuable insights into parental attitudes towards COVID-19 in children, highlighting symptom recognition, transmission awareness, and preventive practices. Correlating these insights with sociodemographic factors underscores the need for tailored educational initiatives, particularly in rural areas, and for addressing gender and socioeconomic disparities. The efficacy of advanced analytics, exemplified by the SVM classifier, underscores the potential for informed decision-making in public health communication and targeted interventions, ultimately empowering parents to safeguard their children's well-being amidst the ongoing pandemic.

Global research trends of virtual simulation technology in public health education and training: A visual analysis based on CiteSpace and VOSviewer

BACKGROUND

In response to global health challenges, implementing innovative educational strategies is crucial for preparing public health professionals with the required skills. This study employed CiteSpace and VOSviewer to visually analyze 3 decades of research on virtual simulation technology in public health education and training. The visual knowledge map created aimed to uncover the research trends, key areas of interest, and emerging frontiers in this domain.

METHODS

Leveraging the Web of Science core collection database and employing bibliometric methods, the CiteSpace and VOSviewer softwares were used to analyze the annual publication, literature distribution, country/institution distribution, author distribution, co-cited literature, and keywords related to the virtual simulation technology in public health education and training over the past 30 years. A visual map was then created.

RESULTS

A total of 1241 research articles were involved in the analysis. The yearly publication count and citations had an overall upward trend, with a significant increase in the number of publications since 2019. The studies in this domain are mainly concentrated in developed countries, particularly in Europe and the United States. The United States emerged as the frontrunner in terms of the number of publications, while both the United Kingdom and the United States exhibited the highest mediation centrality and exerted the greatest international influence. Harvard University made the most substantial contribution with 50 articles and exhibited an institutional cooperation centrality of 0.12. The paper entitled "Using thematic analysis in psychology" had the highest number of citations (28). The primary research hotspots included disaster medicine, telehealth, and virtual reality, while aspects such as digital health, augmented reality, and serious games had consistent continuity. An emerging research frontier, Post-2020, included several topics such as medicine, COVID-19, quality, virtual learning, rehabilitation, depression, and strategy.

CONCLUSION

This study presents the inaugural comprehensive analysis of global trends, hotspots, frontiers, and advancements in the implementation of virtual simulation technology in public health education and training, utilizing CiteSpace and VOSviewer software. The research findings reveal a significant surge in publications since 2019, with a particular emphasis on disaster medicine, telehealth, and virtual reality, indicating the versatility and promise of virtual simulation in the changing educational environments. These findings emphasize the significance of virtual simulation as a dynamic and progressive tool in public health education, proposing a promising direction for future research and practical applications.

Exploring the impact of renewable energy, green taxes and trade openness on carbon neutrality: New insights from BRICS countries

The world faces two significant challenges: promoting sustainable economic growth and reaching carbon neutrality. In BRICS countries, these challenges are shaped by renewable energy, green taxes, and trade openness. These countries were selected for their strategic location and the abundance of relevant data collected over the period of 1990-2021, providing a distinctive window into the energy and economic dynamics of the area. The link between renewable energy consumption, green taxes, trade openness, and natural resources and their effects on carbon emissions in BRICS countries is examined in this study using the Fully Modified Ordinary Least Square Method (FMOLS) estimator and the Drisc Kraay estimator for the robustness test. The findings indicate that using renewable energy and green taxes primarily contribute to reducing emissions, particularly at higher emissions levels. The study reveals that various factors, namely financial globalization, trade openness, efficient resource management, and population growth, substantially impact carbon neutrality. Population growth positively impacts carbon neutrality, while using renewable energy sources mitigates it. Furthermore, the empirical findings show a statistically significant positive association between financial globalization, efficient resource management, and carbon neutrality in BRICS nations. Therefore, it is necessary to implement an integrated ecological governance strategy to control and direct financial resources towards sustainable development and green energy.

Investigating the impact of the internet on managing green financial innovation and improving agricultural conditions in water-scarce Asian regions using ANN modeling

This research focuses on the importance of management strategies, green innovation, and sustainable practices in the agricultural sector. These factors are crucial for job creation, food security, and environmental conservation. Particularly in water-scarce regions, effective management is necessary to overcome natural resource constraints and encourage a shift towards digital agriculture (AGRI). The study aims to identify and analyze the challenges and issues related to agricultural research and technology in Asian countries. The collected data were analyzed using exploratory factor analysis in the SPSS software. The analysis revealed a range of issues and challenges for agricultural development, including those related to the structure and policy framework, the availability and quality of resources and infrastructures, and the provision of effective support services, all of which encompassed factors such as research and technology investment, research management, productivity, research culture, networking, and the integration of higher education and agricultural research. To estimate the efficiency of technology development, agricultural development, and support services for AGRI, an artificial neural network (ANN) was utilized. The ANN was trained by incorporating changes in management strategies, green innovation, and sustainability across a broader range of experimental scenarios. The evaluation of the ANN's predictions showed that improvements in management strategies and the adoption of green innovation and sustainability significantly impacted the productivity of technology development, agricultural development, and support services for AGRI. The accuracy of the ANN's predictions was further assessed using linear regression. The results indicated an acceptable level of error when compared to the target results obtained from experimental tests. Overall, this study emphasizes the importance of effective management, green innovation, and sustainable practices in driving advancements in technology and agricultural development.

Government digitalization and its influence on government functions transformation adopting a Structural Functionalism perspective: Evidence from county-level governments in China

A proactive and innovative approach to government digital reform is necessary to ad-dress potential explanations for the sustained influence of government digitalization, thereby optimizing the behavior and performance of government functions transformation. This study constructs a theoretical framework for understanding the driving mechanisms of government digitalization on government functions transformation from a Structural Functionalism perspective. Based on subdividing government digitalization into four dimensions and government functions transformation into two dimensions, a survey questionnaire with high validity and reliability was compiled and distributed. Utilizing Structural Equation Model, the study investigated the driving mechanisms of government digitalization on government functions transformation and their spatial variations as follows: (1) Digitalization Planning underscores its decisive role in local government functions transformation. (2) Digitalization Technology has a substantial direct impact on Digitalization Platform. (3) Digitalization Policy directly affects Digitalization Platform, behavior, and performance of government functions transformation. (4) Digitalization Platform exerts the most direct impact on government functions transformation. Additionally, the driving mechanisms of government digitalization on government functions transformation exhibit both commonalities and differences across the East, Center, and West of China.

A comprehensive study on unraveling the advances of immersive technologies (VR/AR/MR/XR) in the healthcare sector during the COVID-19: Challenges and solutions

The current COVID-19 pandemic has affected almost every aspect of life but its impact on the healthcare landscape is conspicuously adverse. However, digital technologies played a significant contribution in coping with the challenges spawned by this pandemic. In this list of applied digital technologies, the role of immersive technologies in battling COVID-19 is notice-worthy. Immersive technologies consisting of virtual reality (VR), augmented reality (AR), mixed reality (MR), extended reality (XR), metaverse, gamification, etc. have shown enormous market growth within the healthcare system, particularly with the emergence of pandemics. These technologies supplemented interactivity, immersive experience, 3D modeling, touching sensory elements, simulation, and feedback mechanisms to tackle the COVID-19 disease in healthcare systems. Keeping in view the applicability and significance of immersive technological advancement, the major aim of this study is to identify and highlight the role of immersive technologies concerning handling COVID-19 in the healthcare setup. The contribution of immersive technologies in the healthcare domain for the different purposes such as medical education, medical training, proctoring, online surgeries, stress management, social distancing, physical fitness, drug manufacturing and designing, and cognitive rehabilitation is highlighted. A comprehensive and in-depth analysis of the collected studies has been performed to understand the current research work and future research directions. A state-of-the-artwork is presented to identify and discuss the various issues involving the adoption of immersive technologies in the healthcare area. Furthermore, the solutions to these emerging challenges and issues have been provided based on an extensive literature study. The results of this study show that immersive technologies have the considerable potential to provide massive support to stakeholders in the healthcare system during current COVID-19 situation and future pandemics.

Navigating the innovation policy dilemma: How subnational governments balance expenditure competition pressures and long-term innovation goals

In the pursuit of economic growth, the role of innovation has become increasingly important, posing dilemmas for subnational governments as they navigate the balance between expenditure competition and long-term investments in innovation. This study aimed to investigate the intricate relationship between fiscal pressures and the pursuit of innovation goals faced by regional authorities. To achieve this, a systematic literature review was conducted, synthesizing more than 150 studies published within the past 15 years. Keyword searches were conducted across multiple databases, and additional scholarly articles were incorporated through citation tracking. Rigorous qualitative analysis techniques, including inductive coding and thematic analysis, were employed to distill conceptual insights from the literature. The analysis performed in this review reveals extensive discussions regarding the influence of competition on innovation outcomes, encompassing a wide range of perspectives. The potential advantages of localization are emphasized by some viewpoints, while others caution against the risks of inadequate investment. The effective coordination of policies across multiple levels of governance to maximize synergies between national and subnational innovation systems emerges as a complex yet crucial challenge. It is observed that collaborative networks, which facilitate knowledge exchange through industrial clustering and public-private linkages, play a significant role in leveraging regional innovation assets. Strategic approaches that successfully balance competition with long-term capacity development have been demonstrated by leading jurisdictions. These findings highlight the significance of tailored policy frameworks that account for the unique contexts of each region, providing opportunities to harness competitive motivations while sustaining investments in innovation. Ongoing coordination is essential to strike a balance between responsiveness and coherence across diverse territories. This study offers practical guidance and academic insights on strategies to align decentralized imperatives, aiming to optimize prosperity through knowledge creation within evolving multi-level innovation systems. By shedding light on these strategies, the research contributes to both practical and academic understandings of how to effectively navigate and capitalize on the dynamics of such systems.

The impact of farmers' participation in green cooperative production on green performance-A study based on the moderating effect of environmental regulation

Based on 491 farmers joining in cooperatives microscopic data in Jiangxi Province,the paper uses Ordinary Least Squares to test the influence mechanism of cooperative green production on green performance, and takes environmental regulation as a regulatory variable to explore the relationship between cooperative green production and cooperative green performance. The results have shown that: (1) The green production cooperatives have a significant positive impact on their green performance, and the impact of green production on economic performance, social performance and ecological performance gradually strengthens from weak to strong; (2) Environmental regulations have a positive regulatory effect on the relationship between cooperative green production and cooperative green performance, among which three types of environmental regulations, namely, incentive, restraint and guided, can strengthen the positive relationship between green production and green performance.

Impact of the digital economy on carbon dioxide emissions in resource-based cities

With the rapid development of the digital economy, its environmental impact, particularly on carbon dioxide emissions in resource-based cities, has emerged as a vital research topic. Resource-based cities, often central to traditional industries, are confronted with the dual challenges of environmental pollution and economic transformation. This study employs empirical analysis to examine the influence of the digital economy on carbon dioxide emissions in these cities. The findings reveal that the digital economy significantly reduces carbon dioxide emissions, with this impact being more pronounced in the early stages of digital economic development and gradually diminishing thereafter. In the mechanism analysis, we found that the digital economy can reduce carbon dioxide emissions in resource-based cities by raising public concern about the environment. Moreover, the study highlights significant variations in carbon reduction effects among different types of resource-based cities, noting that stronger environmental regulations further enhance these effects. These insights not only provide a new theoretical perspective but also offer practical guidance for policymakers in promoting sustainable development within the digital economy.

Green credit policy and residents' health: quasi-natural experimental evidence from China

Background

Residents' health plays an important role in economic prosperity and national development.

Methods

The research analyzes data from 262 prefecture-level cities in China spanning the period from 2010 to 2021. Utilizing the implementation of green credit policy in China as a quasi-natural experiment, the paper employs the time-varying Differences-in- Differences (DID) model to evaluate the influence of green credit policy on residents' health.

Results

The paper results show that: (1) the green credit policy significantly improves residents' health, and this conclusion still holds after a series of robustness tests. (2) Mechanism analysis reveals that the green credit policy affects residents' health through the improvements of the environment and the elevation of public services standards in demonstration cities. (3) Heterogeneity analysis shows that the impact of green credit policy on residents' health is more significant in the western cities and resource-based cities than in the central-eastern cities and non-resource-based cities. This paper explains the specific path and realization of green credit policy to enhance residents' health, which provides a reference for further designing and improving effective green credit policy.

Discussion

The deficiencies within the green credit policy has resulted in limited improvements. It is recommended that China should broaden the ambit of the green credit policy and refine the criteria for its execution.

A high-accuracy lightweight network model for X-ray image diagnosis: A case study of COVID detection

The Coronavirus Disease 2019(COVID-19) has caused widespread and significant harm globally. In order to address the urgent demand for a rapid and reliable diagnostic approach to mitigate transmission, the application of deep learning stands as a viable solution. The impracticality of many existing models is attributed to excessively large parameters, significantly limiting their utility. Additionally, the classification accuracy of the model with few parameters falls short of desirable levels. Motivated by this observation, the present study employs the lightweight network MobileNetV3 as the underlying architecture. This paper incorporates the dense block to capture intricate spatial information in images, as well as the transition layer designed to reduce the size and channel number of the feature map. Furthermore, this paper employs label smoothing loss to address the inter-class similarity effects and uses class weighting to tackle the problem of data imbalance. Additionally, this study applies the pruning technique to eliminate unnecessary structures and further reduce the number of parameters. As a result, this improved model achieves an impressive 98.71% accuracy on an openly accessible database, while utilizing only 5.94 million parameters. Compared to the previous method, this maximum improvement reaches 5.41%. Moreover, this research successfully reduces the parameter count by up to 24 times, showcasing the efficacy of our approach. This demonstrates the significant benefits in regions with limited availability of medical resources.

Rational resistance amidst gentle breeze and mild rain: Examining online collective behavior among the Chinese public using the elaborated social identity model

This research examines internet collective behavior in mainland China during the COVID-19 pandemic, focusing on the factors and characteristics that drive such behavior. The Chinese government initially implemented a conservative and biased policy to contain the spread of the virus, but the sudden lifting of lockdown measures in late 2022 resulted in a surge in infections and scarcity of medical resources. This policy shift led many Chinese internet users to perceive the government's actions as hasty and harsh, prompting them to engage in collective online behavior. The study employed a survey-based approach, collecting 1,626 valid questionnaires, which underwent reliability testing, descriptive statistical analysis, and a difference-in-differences test. A structural equation model (SEM) was then constructed and applied to comprehensively analyze the mediating and moderating effects of latent variables. Ethical considerations were prioritized, with informed consent obtained from all participants, who were provided with detailed information about the study and given sufficient time to review and ask questions. The research yielded three primary conclusions: the Chinese public demonstrated a perception of fairness and exhibited obedience, respect, and cooperation with the government during the epidemic; the observed online collective behavior can be characterized as a moderate and rational form of resistance, explained by the elaborated social identity model (ESIM); and the middle class consistently adopted a self-vulnerability strategy, positioning themselves as beneficiaries of protection to maximize their own interests in epidemic prevention and control. This study shows notable insights into internet collective behavior in mainland China during the COVID-19 pandemic, highlighting perceptions, resistance, and strategies adopted by different segments of the population.

Human viral pathogens in the wastewater-source water-drinking water continuum: Evidence, health risks, and lessons for future outbreaks in low-income settings

Human viral pathogens, including SARS-CoV-2 continue to attract public and research attention due to their disruption of society, global health, and the economy. Several earlier reviews have investigated the occurrence and fate of SARS-CoV-2 in wastewater, and the potential to use such data in wastewater-based epidemiology. However, comprehensive reviews tracking SARS-CoV-2 and other viral pathogens in the wastewater-water-drinking water continuum and the associated risk assessment are still lacking. Therefore, to address this gap, the present paper makes the following contributions: (1) critically examines the early empirical results to highlight the occurrence and stability of SARS-CoV-2 in the wastewater-source water-drinking water continuum, (2) discusses the anthropogenic and hydro(geo)logical processes controlling the circulation of SARS-CoV-2 in the wastewater-source water-drinking water continuum, (3) discusses the risky behaviour, drivers and high-risk settings in the wastewater-source water-drinking water continuum, (4) uses the available empirical data on SARS-CoV-2 occurrence in the wastewater-source water-drinking water continuum to discuss human health risks from multiple exposure pathways, gendered aspects of SARS-CoV-2 transmission via shared on-site sanitation systems, and (5) develops and risk mitigation strategy based on the available empirical evidence and quantitative human risk assessment data. Finally, it presents a comprehensive research agenda on SARS-CoV-2/COVID-19 to guide the mitigation of future similar outbreaks in low-income settings.