With the increasing call for sustainable development, cities’ demand for green innovation has also been growing. However, relatively little research summarizes the influencing factors of urban green innovation. In this study, we conducted a visual analysis of 1193 research articles on green innovation in cities from the Web of Science core database using bibliometrics and visualization analysis. By analyzing co-occurrence, co-citation, and high-frequency keywords in the literature, we explored the current research status and development trends of influencing factors of urban green innovation and summarized the research in this field. The study found that collaboration among authors and institutions in this field needs to be strengthened to a certain extent. In addition, the study identified the research hotspots and frontiers in the field of urban green innovation, including “management”, “diffusion”, “smart city”, “indicator”, “sustainable city”, “governance”, and “environmental regulation”. Among them, “management”, “governance”, “indicator”, and “internet” are the research frontiers in this field, which are expected to have profound impacts on the future development of urban green innovation. The co-citation analysis results found that China has the highest research output in this field, followed by the United States, England, Australia, and Italy. In conclusion, this study uses CiteSpace software to identify important influencing factors and development trends of urban green innovation. Urban green innovation has gradually become a norm for social and collective behavior in the process of concretization, interdisciplinary development, and technological innovation. These findings have important reference value for promoting research and practice of urban green innovation.

Nationwide integration of AI into the contemporary art sector has taken place since government AI regulations in 2023 to promote AI use. China’s AI integration into industry is ‘ahead’ of other countries, meaning that other countries can learn from these creative professionals. Consequently, contemporary visual artists have devised arts-led sustainable AI solutions to overcome global AI concerns. They are now putting these solutions into practice to maintain their jobs, arts forms, and industry. This paper draws on 30 interviews with contemporary visual artists, and a survey with 118 professional artists from across China between 2023 and 2024. Findings show that 87% use AI and 76% say AI is useful and they will continue to use AI into the future. Findings show professionals have had time to find DIY, bottom-up solutions to AI concerns, including (1) building strong authorship practices, identity, and brand, (2) showing human creativity and inner thinking, (3) gaining a balanced independent position with AI. They want AI regulations to liberalise and promote AI use so they can freely experiment and develop AI. These findings show how humans are directing the use of AI, altering current narratives on AI-led impacts on industry, jobs, and human creativity.

Objective: Sleep-wake disorders is a common disease in children and adolescents. In recent years, there has been an increasing number of studies on the intervention of exercise therapy in sleep-wake disorders. This study aims to systematically review the development status, research frontiers, research hotspots and development trends of exercise therapy in the through bibliometric methods. Methods: The data comes from the Web of Science Core Collection database. Select all the original data from the establishment of the database to 26 April 2024. Summarize the external characteristics of the literature through Web of Science, Use Excel 2021, Origin 2021, VOS viewers 1.6.20 and Cite Space 6.3.R1 to visually analyze countries/regions, institutions, journals, authors, co-cited references and co-occurrence keywords, use the bibliometric online analysis platform (https://bibliometric.com/) to analyze the changes of keywords and extended keywords over the years. Results: We received a total of 775 publications. The works were sourced from 1429 institutions in 75 countries/regions, published in 113 journals, and written by 4332 authors. The number of publications peaked in 2012, 2018, 2019 and 2021 respectively. In the United States, Harvard University and Children (Basel) have the highest number of publications in this field. The analysis of co-cited references shows that there are three main research frontiers in this field, including 24-hour exercise behavior guidelines for children and adolescents, COVID-19 lockdown and cardiometabolic risk. Screen time, mental health, validity, depression, guidelines, stress, and mediterranean diet are still the current research hotspots in the field, and may become potential research hotspots in the future. Conclusion: The development of research in the field of exercise therapy for children and adolescents with sleep-wake disorders is relatively slow, and there is still a lack of cross-regional scientific research collaborations between countries/regions, institutions and individuals. Our research suggests that it may be a worthwhile research direction to promote the establishment of healthy lifestyle behaviors in the gathering environment of children and adolescents, formulate targeted policies for disease prevention, diagnosis and management, strictly implement preventive measures, improve the level of diagnosis, and dig deep into the precise treatment plan of diseases.

This paper conducts a bibliometric visual analysis of the application of the Unified Theory of Acceptance and Use of Technology (UTAUT) in education, using CiteSpace software. Drawing on data from the Web of Science, the study explores research trends and influential works related to UTAUT from 2008 to 2023. It highlights the growing use of educational technologies such as mobile learning and virtual reality tools. The analysis reveals the most cited articles, journals, and key institutions involved in UTAUT research. Furthermore, keyword analysis identifies research hot spots, such as artificial intelligence and behavioral intentions. This study contributes to the understanding of how UTAUT has been used to predict technology adoption in education and provides recommendations for future research directions based on emerging trends in the digital learning environment.

Preserving roads involves regularly evaluating government policy through advanced assessments using vehicles with specialized capabilities and high-resolution scanning technology. However, the cost is often not affordable due to a limited budget. Road surface surveys are highly expected to use low-cost tools and methods capable of being carried out comprehensively. This research aims to create a road damage detection application system by identifying and qualifying precisely the type of damage that occurs using a single CNN to detect objects in real time. Especially for the type of pothole, further analysis is to measure the volume or dimensions of the hole with a LiDAR smartphone. The study area is 38 province’s representative area in Indonesia. This research resulted in the iRodd (intelligent-road damage detection) for detection and classification per type of road damage in real-time object detection. Especially for the type of pothole damage, further analysis is carried out to obtain a damage volume calculation model and 3D visualization. The resulting iRodd model contributes in terms of completion (analyzing the parameters needed to be related to the road damage detection process), accuracy (precision), reliability (the level of reliability has high precision and is still within the limits of cost-effective), correct prediction (four-fifths of all positive objects that should be identified), efficient (object detection models strike a good balance between being able to recognize objects with high precision and being able to capture most objects that would otherwise be detected-high sensitivity), meanwhile, in the calculation of pothole volume, where the precision level is established according to the volume error value, comparing the derived data to the reference data with an average error of 5.35% with an RMSE value of 6.47 mm. The advanced iRodd model with LiDAR smartphone devices can present visualization and precision in efficiently calculating the volume of asphalt damage (potholes).