How digital economy (DE) empowers high-quality development of tourism (HQDT) has become a common concern among scholars. Given this, this study clarifies the theoretical connotation of DE enabling HQDT,and finds that: Micro, DE promotes efficiency improvements in tourism enterprises, with its economies of scale and Matthew effect reducing average costs, its economies of scope meeting diversified demand, and its long-tail effect improving supply-demand matching mechanism. Meso, DE can transform and upgrade tourism industry structure through industrial digitization and digital industrialization, and also form a new tourist industry form and value chain through cross-border integration. Macro, DE can stimulate innovation and flexibility of market players, increase new factor inputs in tourism, improve factor allocation efficiency, and advance macro regulation of the tourism market. Accordingly, the study conducts an empirical test based on panel data for 31 provinces in mainland China during 2011-2020. Results show that: ① DE positively influences HQDT, and the sub-dimensions all positively influence HQDT. ② DE has a heterogeneous impact on HQDT and shows spatial spillover effects. Finally, the study concludes with effective paths for DE promoting HQDT: "Promote digital infrastructure construction, accelerate tourism digital transformation, strengthen integration and innovation development, and overcome the challenges of tourism enterprises".
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11065292 | PMC |
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0303087 | PLOS |
Neural Netw
January 2025
School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510640, China; Key Laboratory of Autonomous Systems and Network Control, Ministry of Education, South China University of Technology, Guangzhou, 510640, China; Institute for Super Robotics (Huangpu), Guangzhou, 510555, China; Nanchang University, Nanchang, 330031, China; College of Computer Science and Engineering, Jishou University, Jishou, 416000, China; Guangdong Artificial Intelligence and Digital Economy Laboratory (Pazhou Lab), Guangzhou, 510335, China; School of Electronical Engineering, Shaanxi University of Technology, Hanzhong, 723001, China; School of Information Science and Engineering, Changsha Normal University, Changsha, 410100, China; Institute of Artificial Intelligence and Automation, Guangdong University of Petrochemical Technology, Maoming, 525000, China. Electronic address:
To address the challenge of low recognition accuracy in transformer fault detection, a novel method called swarm budorcas taxicolor optimization-based multi-support vector (SBTO-MSV) is proposed. Firstly, a multi-support vector (MSV) model is proposed to realize multi-classification of transformer faults based on dissolved gas data. Then, a swarm budorcas taxicolor optimization (SBTO) algorithm is proposed to iteratively search the optimal model parameters during MSV model training, so as to obtain the most effective transformer fault diagnosis model.
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Black Dog Institute, University of New South Wales, Sydney, Australia.
Background: With increasing adoption of remote clinical trials in digital mental health, identifying cost-effective and time-efficient recruitment methodologies is crucial for the success of such trials. Evidence on whether web-based recruitment methods are more effective than traditional methods such as newspapers, media, or flyers is inconsistent. Here we present insights from our experience recruiting tertiary education students for a digital mental health artificial intelligence-driven adaptive trial-Vibe Up.
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Department of Healthcare Economics and Quality Management, School of Public Health, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
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View Article and Find Full Text PDFBMJ Open
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University of St Gallen, St Gallen, Switzerland.
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Clinical Sciences, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.
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