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In many instances, the data exhibits periodic and trend characteristics. However, indices like the Digital Economy Development Index (DEDI), which pertains to science, technology, policy and economy, may occasionally display erratic behaviors due to external influences. Thus, to address the unique attributes of the digital economy, this study integrates the principle of information prioritization with nonlinear processing techniques to accurately forecast rapid and anomalous data.

The proposed method utilizes the new information priority GM(1,1) model alongside an optimized BP neural network model achieved through the gradient descent technique (GD-BP). Initially, the provincial Digital Economic Development Index (DEDI) is derived using the entropy weight approach. Subsequently, the original GM(1,1) time response equation undergoes alteration of the initial value, and the time parameter is fine-tuned using Particle Swarm Optimization (PSO). Next, the GD-BP model addresses the residual error. Ultimately, the prediction outcome of the grey combination forecasting model (GCFM) is derived by merging the findings from both the NIPGM(1,1) model and the GD-BP approach.

Using the DEDI of Jiangsu Province as a case study, researchers demonstrate the effectiveness of the grey combination forecasting model. This model achieves a mean absolute percentage error of 0.33%, outperforming other forecasting methods.

First of all, due to the limited data access, it is impossible to obtain a more comprehensive dataset related to the DEDI of Jiangsu Province. Secondly, according to the test results of the GCFM from 2011 to 2020 and the forecasting results from 2021 to 2023, it can be seen that the results of the GCFM are consistent with the actual development situation, but it cannot guarantee the correctness of the long-term forecasting, so the combination forecasting model is only suitable for short-term forecasting.

This article proposes a grey combination prediction model based on the principles of new information priority and nonlinear processing.

Rapid innovation and developments in personal electronic technology have encouraged users to change users' devices more frequently than ever, which has resulted in creating a massive increase in the amount of electronic waste. The study focuses on identifying the barriers to closed-loop supply chain (CLSC) in the electronic industry.

A framework for analyzing the relationships among CLSC adoption barriers is designed. The authors adopted the decision-making trial and evaluation laboratory (DEMATEL) technique to determine the critical barriers of electronic CLSC from the opinion of experts in the field.

The outcome from the analysis suggests that cost barriers, financial barrier, process barriers and supplier-side barriers are the main causal factors that prevent the adoption and implementation of e-waste CLSC. The causal relationship indicates that financial barrier is the most influential factor, while phycological barrier is the most flexible barrier to the adoption of e-waste CLSC.

This study is restricted to CLSC adoption barriers in the electronic industry by evaluating 36 sub-barriers grouped into 8 main dimensions related to different members of the supply chain.

Closed-loop adoption barriers have been proposed to understand the crucial barriers to implementation of CLSC in the electronic industry. The cause-and-effect relationship indicates the critical factors to be improved to increase adoption of e-waste CLSC, helping managers and regulatory bodies to mitigate the problem areas.

This study contributes to the literature on CLSC by adopting a multi-criteria decision-making (MCDM) technique which captures the critical barriers of e-waste CLSC adoption in Indian scenario.

Microblogs are communication platforms for companies and consumers that challenge companies' brand marketing strategies. This paper provides a theoretical basis for expanding microblog applications and a practical basis for improving the effectiveness of brand marketing.

The authors use factor analysis to extract the factors of microblog user influence and construct a structural equation model to reveal the interaction mechanism of the influencing factors. Additionally, the authors clarify the promotion and enhancement effects of these factors.

Microblog user influence can be converted into richness, interaction and value factors. The richness factor significantly affects the latter two, whereas the interaction factor does not affect the value factor.

First, the sample used is limited to media industry practitioners. To increase generalizability, diverse groups should be included in future studies. Second, this model's theoretical explanatory ability can be further developed by adding other meaningful factors beyond the existing ones.

This study analyzes the factors of microblog user influence in China and validates the relevant elements. As a result, it improves the influence research on social media users and benefits the practice of information recommendation and microblog marketing.

The construction industry has experienced an irreversible digital transformation to smart construction. Many countries have published supporting policies to encourage the development of smart construction. However, there is no universally valid approach. This paper thus aims to evaluate smart construction policies issued by 24 pilot cities in China and identify applicable policy tools and their impact.

This paper collected 33 governmental documents on smart construction through the official websites in China. Different policy tools were classified into supply-side, demand-side and environment-side categories. The supporting policies of smart construction development in pilot cities were quantitatively evaluated by using a policy modeling consistency index (PMC-index) model.

Supply-type and environment-type policy instruments were used more frequently than demand-type policies in 24 pilot cities. Most of the 24 pilot cities had an evaluation of PMC-index over 8, realizing the consistency of smart construction policies. Eight pilot cities had an evaluation of PMC-index of 6–7.99, realizing acceptable consistency. Only Foshan City has an evaluation of PMC-index below 4, which may reflect a poor consistency of policy implementation. The paper proposes consistencies of smart construction policies of 24 pilot cities and valid policy instruments, including the presale of commercial residential buildings, additional bonus points in the tendering process and cooperating with multiple departments when promoting smart construction.

This paper contributes to expanding policy evaluation studies in the smart construction field and provides concrete suggestions for policymakers to formulate more effective and specific policies and strategies for the development of smart construction.

The transition from fossil fuel-based energy systems to renewable energy sources, commonly referred to as the energy transition, is essential for combating climate change. However, comprehensive studies that thoroughly examine the financial mechanisms involved in this process are lacking. Despite the availability of various financial tools, there is a notable absence of extensive research that synthesizes and categorizes these mechanisms into broad groups.

A systematic literature review is used to explore a comprehensive framework for financial mechanisms related to the energy transition and their application across six stages of the process.

The framework of financial mechanisms for energy transition encompasses these six factors: public financing mechanisms, private financing mechanisms, market-based mechanisms, innovative financing mechanisms, risk mitigation instruments and institutional support and capacity building.

This is the first study that thoroughly reviewed the financial mechanisms involved in the energy transition process.

This systematic review aims to identify the key determinants of knowledge transfer (KT) activities among researchers.

This study systematically reviewed KT literature in academic settings from 1995–2023. The authors searched Web of Science and Scopus using predefined keywords, following PRISMA guidelines for screening and eligibility assessment. From 158 selected articles, the authors extracted data and conducted a descriptive analysis to map KT activities’ evolution. A narrative synthesis approach categorized determinants of researchers’ KT activities.

The systematic review findings revealed a general conceptual framework that categorizes the identified determinants of KT into four categories. At the individual level, the factors are related to the sociodemographic characteristics of the researcher (e.g. gender, age, experience), their psychological aspects (e.g. attitude, intrinsic motivation, intention) and personal characteristics (e.g. self-efficacy, communication skills). At the research team level, leadership style and team dynamics. At the organizational level, the findings emphasize university characteristics (e.g. size, structure and ranking), KT culture installed and university resources. At the inter-organizational level, the key determinants were funding sources, network strength and trust.

The studies included in our database were different in terms of contexts, country of the study, the disciplines of KT and the types of KT activities examined. This variety restricts the direct comparison of research findings thus the generalizability of our conclusions. Future research should focus on specific contexts, disciplines, countries or types of KT activities to provide generalizable findings.

A better understanding of all the factors influencing KT among university researchers is essential for several reasons. First, it will enable the government to develop effective policies to promote KT ecosystems. Second, universities can create strategies, policies and programs to support researchers’ engagement in KT activities. Finally, researchers can be more strategic in their KT efforts.

This systematic review contributes to the literature by providing a comprehensive conceptual framework that identifies KT determinants at different levels and fills a gap in the existing literature that only addresses specific aspects of KT determinants. This framework can be a theoretical reference for future empirical studies. Furthermore, it practically provides recommendations for different actors including, government, universities and researchers.

This paper aims to assess the current and future dynamics of land cover transitions and analyze the vegetation conditions in Abuja city since its establishment as the capital of Nigeria in 1991.

A random forest classifier embedded in the Google Earth Engine platform was used to classify Landsat imagery for the years 1990, 2001, 2014 and 2020. A post-classification comparison was used to detect the dynamics of land cover transitions. A hybrid simulation model that comprised cellular automata and Markovian was used to model the probable scenario of land cover changes for 2050. The trend of Normalized Difference Vegetation Index was examined using Mann–Kendall and Theil Sen’s from 2014 to 2022. Nighttime band data from the National Oceanic and Atmospheric Administration were obtained to analyze the trend of urbanization from 2014 to 2022.

The findings show that built-up areas increased by 40%, while vegetation, bare land and agricultural land decreased by 27%, 7% and 8%, respectively. Vegetation had the highest declining rate at 3.15% per annum. Built-up areas are expected to increase by 17.1% between 2020 and 2050 in contrast with other land cover. The proportion of areas with moderate vegetation improvement is estimated to be 15.10%, while the proportion of areas with no significant change was 38.10%. The overall proportion of degraded areas stands at 46.8% due to urbanization.

The findings provide a comprehensive insight into the dynamics of land cover transitions and vegetation variability induced by rapid urbanization in Abuja city, Nigeria. In addition, the findings provide valuable insights for policymakers and urban planners to develop a sustainable land use policy that promotes inclusivity, safety and resilience.

This article aims to map the expectations of manufacturing companies towards suppliers in terms of implementing improvement activities. The article poses two research questions: RQ1: What kind of improvement of activities do the surveyed producers expect from their suppliers? RQ2: Do factors such as size, capital or implemented systems influence different assessments of the analyzed requirements toward suppliers?

The Computer Assisted Telephone Interview (CATI) technique was used to collect data. The sample consists of 150 producers (employing over 50 people) who were suppliers for enterprises from the automotive, electromechanical and chemical sectors operating in the Polish business-to-business (B2B) market. We analyzed 11 improvement activities, while their correlation structure was examined by exploratory factor analysis.

We have identified three latent factors – risk reduction, product innovation and increasing efficiency – which summarize the main expectations of manufacturing companies towards suppliers. Expectations for these factors are independent of the implemented management system, although the analysis showed higher expectations for product innovation in organizations with the implementation of Kaizen.

The article fills the research gap in the literature. The research results presented in the literature so far have focused on the expectations of enterprises towards suppliers in terms of meeting the criteria for their initial and periodic assessment. The research gap in the article is the result of empirical research presenting the expectations of manufacturers towards suppliers in terms of improving their processes. Based on the findings of the presented study, development trends and implications for managers responsible for purchasing processes and relationships with suppliers can be determined.

The purpose of this study is to analyze the current situation of the competitiveness development of China’s marine industrial clusters, reveal the existing problems and challenges, provide theoretical support and practical guidance for improving the competitiveness of China’s marine industrial clusters so as to promote industrial upgrading and high-quality development and help China realize the strategic transformation from a marine power to a marine power.

This report first provides a detailed review of the current development status and existing issues of marine industry clusters in China. Second, it constructs an evaluation index system for the competitiveness development of China’s marine industry clusters and conducts competitiveness analysis and evaluation of typical marine industry clusters in China. Third, it explores the development trends and prospects of typical marine industry clusters in China. Finally, the report proposes countermeasures and suggestions for enhancing the competitiveness of marine industry clusters in China, focusing on resource optimization, cluster structure and cluster efficiency.

(1) Significant competitiveness of marine shipbuilding and ocean engineering equipment clusters: Through technological innovation and policy support, the marine shipbuilding and ocean engineering equipment clusters have shown a marked improvement in competitiveness, advancing toward high-quality development despite facing macroeconomic fluctuations. (2) Continuous improvement in marine energy and offshore wind power clusters: The competitiveness of marine energy and offshore wind power clusters has been continuously enhanced under supportive policies. However, there is still a need to optimize resource allocation and strengthen innovation stability to meet market challenges. (3) Strong growth potential of desalination and comprehensive utilization clusters: The desalination and comprehensive utilization clusters demonstrate robust growth potential in technological innovation and regional collaborative development. Future efforts should focus on the application of environmentally friendly and energy-saving technologies to ensure a balance between economic and ecological benefits.

By strengthening the competitiveness of industrial clusters, China can effectively respond to international competition, accelerate the transformation and upgrading of the marine industry and support its transition from a maritime power to a strong maritime nation. Hence, this study will focus on analyzing the competitiveness development of China’s marine industry clusters, identifying existing problems and challenges and providing theoretical support and practical guidance for enhancing the competitiveness of China’s marine industry clusters.

The purpose of this study was to validate the feasibility of the proposed microstructure-based model by comparing the simulation results with experimental data. The study also aimed to investigate the relationship between the orientation of graphite flakes and the failure behavior of the material under compressive loads as well as the effect of image size on the accuracy of stress–strain behavior predictions.

This paper presents a microstructure-based model that utilizes the finite element method (FEM) combined with representative volume elements (RVE) to simulate the hardening and failure behavior of ferrite-pearlite matrix gray cast iron under uniaxial loading conditions. The material was first analyzed using optical microscopy, scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS) and X-ray diffraction (XRD) to identify the different phases and their characteristics. High-resolution SEM images of the undeformed material microstructure were then converted into finite element meshes using OOF2 software. The Johnson–Cook (J–C) model, along with a damage model, was employed in Abaqus FEA software to estimate the elastic and elastoplastic behavior under assumed plane stress conditions.

The findings indicate that crack initiation and propagation in gray cast iron begin at the interface between graphite particles and the pearlitic matrix, with microcrack networks extending into the metal matrix, eventually coalescing to cause material failure. The ferritic phase within the material contributes some ductility, thereby delaying crack initiation.

This study introduces a novel approach by integrating microstructural analysis with FEM and RVE techniques to accurately model the hardening and failure behavior of gray cast iron under uniaxial loading. The incorporation of high-resolution SEM images into finite element meshes, combined with the J–C model and damage assessment in Abaqus, provides a comprehensive method for predicting material performance. This approach enhances the understanding of the microstructural influences on crack initiation and propagation, offering valuable insights for improving the design and durability of gray cast iron components.