Predicting potential knowledge convergence of solar energy: bibliometric analysis based on link prediction model

Predicting green technology innovation in the construction field from a technology convergence perspective: A two-stage predictive approach based on interpretable machine learning

The construction industry, as a major global energy consumer and carbon emitter, plays a crucial role in achieving global sustainability. A key strategy for the green transformation of this industry-without compromising development-involves fostering green technology innovation. Nevertheless, existing studies exhibit a notable gap in identifying and evaluating potential green technology innovation opportunities within the construction field, leading to a scarcity of decision-making information for governments and innovation entities during the research and development stage. Recognizing this, our study proposes a two-stage technology opportunity prediction approach based on interpretable machine learning from the perspective of technology convergence. Diverging from previous methods, it not only predicts the probability of technology opportunity occurrence but also forecasts the technical impact of convergence opportunities. By analysing 600,442 patent documents in the green and construction fields, we identify 305 high-potential technology convergence opportunities. Our results reveal that technologies such as carbon capture and storage, pollution alarms, solar energy, forestry techniques, wind energy, energy-saving methods, and waste materials for water treatment have significant potential for convergence with construction technologies. Additionally, we analyse the influencing factors behind these convergence innovations, finding that technical similarity and proximity play crucial roles. These findings provide robust decision support for governments and industry stakeholders in formulating scientifically grounded green technology innovation strategies, thereby accelerating the green transformation of the construction industry and contributing to the goal of sustainable development.

Mapping the field: A bibliometric literature review on technology mining

Technology mining (or tech mining, TM) is an emerging research field in science, technology, and innovation studies. However, due to the rapid increase and widespread application of TM research, accurately capturing research topics and emerging developments in TM has become a challenge for scholars. Therefore, this bibliometric literature review combines quantitative methods and content analysis to explore the research foundation and development frontiers of TM and distinguish emerging research topics from relatively mature ones, aiming to deepen the understanding. More specifically, it utilizes co-citation analysis and bibliographic coupling techniques to analyze the TM publication dataset. The results indicate that TM research is mainly based on four foundational areas, and there are five current frontier clusters. Emerging topic detection further shows that technology topic analysis, technology opportunity analysis, and technology management and decision support are currently emerging TM research topics.

Global Progress in Oil and Gas Well Research Using Bibliometric Analysis Based on VOSviewer and CiteSpace

Studies related to oil and gas wells have attracted worldwide interest due to the increasing energy shortfall and the requirement of sustainable development and environmental protection. However, the state of oil and gas wells in terms of research characteristics, technological megatrends, article-produced patterns, leading study items, hot topics, and frontiers is unclear. This work is aimed at filling the research gaps by performing a comprehensive bibliometric analysis of 6197 articles related to oil and gas wells published between 1900 and 2021. VOSviewer and CiteSpace software were used as the main data analysis and visualization tools. The analysis shows that the annual variation of article numbers, interdisciplinary numbers, and cumulative citations followed exponential growth. Oil and gas well research has promoted the expansion of research fields such as engineering, energy and fuels, geology, environmental sciences and ecology, materials science, and chemistry. The top 10 influential studies mainly focused on shale gas extraction and its impact on the environment. More studies were produced by larger author teams and inter-institution collaborations. Elkatatny and Guo have greatly contributed to the application of artificial intelligence in oil and gas wells. The two most contributing institutions were the Southwest Petr Univ and China Univ Petr from China. The People’s Republic of China, the US, and Canada were the countries with the most contributions to the development of oil and gas wells. The authoritative journal in engineering technology was J Petrol Sci Eng, in environment technology was Environ Sci Technol, in geology was Aapg Bull, and in materials was Cement Concrete Res. The keyword co-occurrence network cluster analysis indicated that oil well cement, new energy development, machine learning, hydraulic fracturing, and natural gas and oil wells are the predominant research topics. The research frontiers were oil extraction and its harmful components (1992–2016), oil and gas wells (1997–2016), porous media (2007–2016), and hydrogen and shale gas (2012–2021). This paper comprehensively and quantitatively analyzes all aspects of oil and gas well research for the first time and presents valuable information about active and authoritative research entities, cooperation patterns, technology trends, hotspots, and frontiers. Therefore, it can help governments, policymakers, related companies, and the scientific community understand the global progress in oil and gas well research and provide a reference for technology development and application.

A Review of Technological Forecasting from the Perspective of Complex Systems

Technology forecasting (TF) is an important way to address technological innovation in fast-changing market environments and enhance the competitiveness of organizations in dynamic and complex environments. However, few studies have investigated the complex process problem of how to select the most appropriate forecasts for organizational characteristics. This paper attempts to fill this research gap by reviewing the TF literature based on a complex systems perspective. We first identify four contexts (technology opportunity identification, technology assessment, technology trend and evolutionary analysis, and others) involved in the systems of TF to indicate the research boundary of the system. Secondly, the four types of agents (field of analysis, object of analysis, data source, and approach) are explored to reveal the basic elements of the systems. Finally, the visualization of the interaction between multiple agents in full context and specific contexts is realized in the form of a network. The interaction relationship network illustrates how the subjects coordinate and cooperate to realize the TF context. Accordingly, we illustrate suggest five trends for future research: (1) refinement of the context; (2) optimization and expansion of the analysis field; (3) extension of the analysis object; (4) convergence and diversification of the data source; and (5) combination and optimization of the approach.

Global Trends and Research Hotspots in Long COVID: A Bibliometric Analysis

Long COVID is a condition distinguished by long-term sequelae that occur or persist after the convalescence period of COVID-19. During the COVID-19 pandemic, more and more people who tested positive for SARS-CoV-2 experienced long COVID, which attracted the attention of researchers. This study aims to assess the pattern of long COVID research literature, analyze the research topics, and provide insights on long COVID. In this study, we extracted 784 publications from Scopus in the field of long COVID. According to bibliometric analysis, it is found that: developed countries in Europe and America were in leading positions in terms of paper productivity and citations. The International Journal of Environmental Research and Public Health and the Journal of Clinical Medicine were leading journals in the perspective of publications count, and Nature Medicine had the highest number of citations. Author Greenhalgh T has the highest number of papers and citations. The main research topics were: pathophysiology, symptoms, treatment, and epidemiology. The causes of long COVID may be related to organ injury, inflammation, maladaptation of the angiotensin-converting enzyme 2 (ACE2) pathway, and mental factors. The symptoms are varied, including physical and psychological symptoms. Treatment options vary from person to person. Most patients developed at least one long-term symptom. Finally, we presented some possible research opportunities.

Analyzing the Evolution of Interdisciplinary Areas

Recently, various new areas of research have been of great interest to researchers. As these areas are highly based on academic and industrial needs, it is necessary to examine the change and evolution in research. This study proposed a framework for identifying emerging areas and their evolution. The proposed framework suggests that latent Dirichlet allocation is applied to identify emerging topics and their networks in such interdisciplinary areas. The simulation for empirical network analysis was then applied to the identified topic networks to terminate continuous evolution. The proposed framework is applied to a smart city, which is one of the most interdisciplinary and fast-evolving areas. These findings indicate that the evolution of smart transportation and smart grids is likely to be the focus. The findings also indicate that newly emerging research may lack openness and diversity. This study contributes to further investigate research trends and planning research strategies for new and interdisciplinary areas.