With the outbreak of the COVID-19 pandemic in 2019, educational activities have faced significant disruptions, leading to a widespread adoption of online teaching and a transformation in the evaluation of teaching quality. Using CiteSpace visualization software, the study examines 1485 papers from the Chinese database of China Knowledge Network and 1656 papers from the English database of Web of Science (WoS) spanning the period from January 2013 to June 2023 as research samples. The findings reveal heightened activity in China and other countries research on teaching quality evaluation, moreover, research in both contexts predominantly comprises independent studies, supplemented by collaborative efforts. Notably, there is an increased focus on the exploration of online teaching quality evaluation, specifically delving into methodologies and systems. The emphasis has shifted towards students’ learning initiatives and a comprehensive evaluation of teachers’ work before, during and after class. While research in other countries has also identified new hotspots related to online teaching, the number of studies is comparatively limited. The study proposes the imperative need to update the evaluation criteria for online teaching and enhance the infrastructure of online teaching platforms. Additionally, it advocates for reforms in the evaluation systems of educational institutions and innovations of teachers’ instructional methods.

Tourism experiences are inherently multisensory, engaging visitors’ senses of sight, sound, smell, taste, and touch. This study addresses the gap in literature by investigating the impact of visual and auditory landscapes on tourist emotions and behaviors within coastal tourism settings, using the Stimulus-Organism-Response (SOR) model. Data collected from tourists in Sanya, China, were analyzed using structural equation modeling. The results indicate that both visualscape and soundscape significantly influence tourist emotions (pleasure and arousal) and subsequent loyalty. Pleasure and arousal mediate the relationships between environmental stimuli and tourist loyalty, emphasizing their roles as emotional bridges between the environment and behaviors. These findings highlight the importance of integrating local cultural and community elements into tourism to enhance socio-economic benefits and ensure sustainable development. By fostering a deep connection between tourists and the local environment, these sensory experiences support the preservation of cultural heritage and promote sustainable tourism practices, aligning with the goals of economic development and public policy. The study contributes to the theoretical understanding of multisensory tourism by integrating the SOR model in coastal tourism and emphasizes the roles of visual and auditory stimuli. Practically, it provides insights for tourism managers to improve tourist experiences and loyalty through careful management of sensory elements. This has implications for infrastructure development, particularly in enhancing the quality of soft infrastructure such as cultural and social systems, which are crucial for sustainable tourism and community well-being. Future research could include additional sensory dimensions and diverse destinations for a comprehensive understanding of sensory influences on tourist behaviors and emotions. This research aligns with the broader goals of the policy and development by addressing critical aspects of infrastructure and socio-economic development within the tourism sector.

 Distance education (DE) has recently become a noteworthy study topic in the public education system. From the Web of Science database, 5719 articles discussing DE and published in the period of 2011–2023 were acquired. By analyzing the overall characteristics, co-citation, and keyword co-occurrence of the selected articles, which utilized Cite Space software, the history of DE could be systematically grasped, thereby reasonably predict the emphases of future development. We found that the number of papers relevant to DE had been rapidly growing since 2018. USA, China, and Turkey are the top three countries where most authors or teams were located. The map of keyword cooccurrence showed that the previous DE research mainly focused on telelearning, adult learning, and distributed learning environment. The recent burst words emerging are used to determine that distance education will continue to be studied in the field with high explosive keywords such as visual tracking, technology acceptance model, and user interface. This will provide suggestions and directions for the development of distance education.

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).