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This chapter uses data from the Scopus database to present a comprehensive bibliometric analysis of fintech research, focusing on publication trends, citation patterns, and thematic clusters within the field. The analysis reveals notable trends, including influential publications, prolific authors, and their affiliations. It identifies and explores publications categorized into 23 distinct themes, representing key areas of inquiry in fintech, such as technological advancements, financial inclusion, innovation, data analytics, sustainability, and regulatory compliance. The study also identifies research gaps, indicating areas within fintech that have received limited scholarly attention. These findings provide valuable insights for researchers, policymakers, and industry practitioners. Researchers can better understand the fintech landscape, identify research gaps, and guide future inquiries. Policymakers can develop effective regulations and policies based on identified trends and challenges. Industry practitioners can leverage fintech developments to enhance their strategies and operations.

The purpose of this study is to identify the capacity needs of building professionals to deliver environmentally sustainable buildings (ESBs) globally, from a unique systems approach. Through a review of extant literature, this study contributes to knowledge of the global delivery of ESBs by identifying research trends and gaps that can be tackled in future research, and current hotspots in capacity building (CB) research within the built environment (BE). The adopted systems approach to CB postulates that the construction industry is systemized in nature, and thus, CB solutions within the industry should be approached from a systems approach.

A literature search was conducted using the Scopus search engine, augmented by Google Scholar and Web of Science, to produce 54 relevant articles for analysis. The scientometric analysis was undertaken with the use of VOSViewer to better understand the broad literature on CB in the construction industry which would not have been possible with traditional data analysis. The content analysis allowed, for a systematic review of selected articles, to reveal key themes in this study.

Through a content analysis, four levels of CB were identified within the construction industry; individual level, organizational level, industry level and state level. Nine sub-capacities were subsumed under the four identified levels adopted from Potter and Brough (2004), including but not limited to Performance capacity, Workload capacity, Supervisory capacity, Industry Role capacity and Systems capacity. Under each sub-capacity, key questions were posed to aid identify the capacity needs of BE professionals. A framework for identifying capacity needs in the BE is proposed.

The findings of this study serve as a useful reference for practitioners and policymakers to assess their level of commitment to CB efforts for ESB delivery. The findings of this study have revealed that building the capacities of BE professionals to deliver ESBs should be addressed as part of a broader framework, interdependent on the other levels of CB in the systemic construction industry.

As a review study identifying capacity needs for BE professionals to deliver ESBs, this study enhances knowledge of CB within the construction industry.

This paper aims to provide an overview of the application of blockchain to agri-food supply chains, including key issues and trends. It examines the state of the art and conceptual structure of the field and proposes an agenda to guide future research.

This article performs a bibliometric analysis using VOSviewer software on a sample of 205 articles from the WoS database to identify research trend topics.

The number of publications in this area has increased since 2020, which shows a growing research interest. The research hotspots are related to the integration of blockchain technology in the agri-food supply chain for traceability, coordination between all actors involved, transparency of operations and improvement of food safety. Furthermore, this is linked to sustainability and the achievement of the sustainable development gtoals (SDGs), while addressing key challenges in the implementation of blockchain-based technologies in the agri-food supply chain.

The application of blockchain in the agri-food supply chain may consider four key aspects. Firstly, the implementation of blockchain can improve the traceability of food products. Secondly, this technology supports sustainability issues and could avoid disruptions in the agri-food supply chain. Third, blockchain improves food quality and safety control throughout the supply chain. Fourthly, the findings show that regulation is needed to improve trust between stakeholders.

The paper provides a comprehensive overview of the blockchain phenomenon in the agri-food supply chain by optimising the search criteria. Moreover, it serves to bridge to future research by identifying gaps in the field.

Autonomous flight of unmanned aerial vehicles (UAVs) in global position system (GPS)-denied environments has become an increasing research hotspot. This paper aims to realize the indoor fixed-point hovering control and autonomous flight for UAVs based on visual inertial simultaneous localization and mapping (SLAM) and sensor fusion algorithm based on extended Kalman filter.

The fundamental of the proposed method is using visual inertial SLAM to estimate the position information of the UAV and position-speed double-loop controller to control the UAV. The motion and observation models of the UAV and the fusion algorithm are given. Finally, experiments are performed to test the proposed algorithms.

A position-speed double-loop controller is proposed, by fusing the position information obtained by visual inertial SLAM with the data of airborne sensors. The experiment results of the indoor fixed-points hovering show that UAV flight control can be realized based on visual inertial SLAM in the absence of GPS.

A position-speed double-loop controller for UAV is designed and tested, which provides a more stable position estimation and enabled UAV to fly autonomously and hover in GPS-denied environment.

This study aims to conduct performance and clustering analyses with the help of Digital Government Reference Library (DGRL) v16.6 database examining the role of emerging technologies (ETs) in public services delivery.

VOSviewer and SciMAT techniques were used for clustering and mapping the use of ETs in the public services delivery. Collecting documents from the DGRL v16.6 database, the paper uses text mining analysis for identifying key terms and trends in e-Government research regarding ETs and public services.

The analysis indicates that all ETs are strongly linked to each other, except for blockchain technologies (due to its disruptive nature), which indicate that ETs can be, therefore, seen as accumulative knowledge. In addition, on the whole, findings identify four stages in the evolution of ETs and their application to public services: the “electronic administration” stage, the “technological baseline” stage, the “managerial” stage and the “disruptive technological” stage.

The output of the present research will help to orient policymakers in the implementation and use of ETs, evaluating the influence of these technologies on public services.

The research helps researchers to track research trends and uncover new paths on ETs and its implementation in public services.

Recent research has focused on the need of implementing ETs for improving public services, which could help cities to improve the citizens’ quality of life in urban areas. This paper contributes to expanding the knowledge about ETs and its implementation in public services, identifying trends and networks in the research about these issues.

Unlocking the potential of Big Data Analytics (BDA) has proven to be a transformative factor for the Architecture, Engineering and Construction (AEC) industry. This has prompted researchers to focus attention on BDA in the AEC industry (BDA-in-AECI) in recent years, leading to a proliferation of relevant research. However, an in-depth exploration of the literature on BDA-in-AECI remains scarce. As a result, this study seeks to systematically explore the state-of-the-art review on BDA-in-AECI and identify research trends and gaps in knowledge to guide future research.

This state-of-the-art review was conducted using a mixed-method systematic review. Relevant publications were retrieved from Scopus and then subjected to inclusion and exclusion criteria. A quantitative bibliometric analysis was conducted using VOSviewer software and Gephi to reveal the status quo of research in the domain. A further qualitative analysis was performed on carefully screened articles. Based on this mixed-method systematic review, knowledge gaps were identified and future research agendas of BDA-in-AECI were proposed.

The results show that BDA has been adopted to support AEC decision-making, safety and risk assessment, structural health monitoring, damage detection, waste management, project management and facilities management. BDA also plays a major role in achieving construction 4.0 and Industry 4.0. The study further revealed that data mining, cloud computing, predictive analytics, machine learning and artificial intelligence methods, such as deep learning, natural language processing and computer vision, are the key methods used for BDA-in-AECI. Moreover, several data acquisition platforms and technologies were identified, including building information modeling, Internet of Things (IoT), social networking and blockchain. Further studies are needed to examine the synergies between BDA and AI, BDA and Digital twin and BDA and blockchain in the AEC industry.

The study contributes to the BDA-in-AECI body of knowledge by providing a comprehensive scope of understanding and revealing areas for future research directions beneficial to the stakeholders in the AEC industry.

China’s education has rapidly developed since 1949. Although much has been written about reforms in China’s preschool and primary education policies, less attention has been given to the transition-to-primary school policies.

This study examines the evolution of these policies from 1949 onward, analysing 61 policy documents from the PKULaw.

Four phases are identified: minimal political attention (1949–1982), steady policy releases (1983–2004), stagnation (2005–2010) and frequent updates (since 2011). Policy changes align with educational development, reflecting a shift from academic readiness to overall development. Increased government involvement has led to more diverse themes and more cooperation among stakeholders. The role of the preschool class has evolved from basic support for children without kindergarten access to an institution for early academic learning, prompting policies to restrict it.

These findings provide valuable insights for optimizing China’s educational policies and offer lessons for other nations facing similar challenges in transitioning children from preschool to primary school.

The purpose of this paper is to construct a grey incidence model for panel data that can reflect the incidence direction and degree between indicators.

Firstly, this paper introduces the concept of a negative matrix and preprocesses the data of each indicator matrix to eliminate differences in dimensions and magnitudes between indicators. Then a model is constructed to measure the incidence direction and degree between indicators, and the properties of the model are studied. Finally, the model is applied to a practical problem.

The grey-directed incidence degree is 1 if and only if corresponding elements between the feature indicator matrix and the factor indicator matrix have a positive linear relationship. This degree is −1 if and only if corresponding elements between the feature indicator matrix and the factor indicator matrix have a negative linear relationship.

The example shows the number of days with good air quality is negatively correlated with the annual average concentration of each pollutant index. PM_2.5, PM₁₀ and O₃ are the main pollutants affecting air quality in northern Henan.

This paper introduces the negative matrix and constructs a model from the holistic perspective to measure the incidence direction and level between indicators. This model can effectively measure the incidence between the feature indicator and factor indicator by integrating information from the point, row, column and matrix.