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As digital technology evolves rapidly, smart tourism has become a significant trend in the modernization of the industry, relying on advanced tools like big data and cloud computing to improve travelers' experiences. Despite the growing use of human-computer interaction in museums, there remains a lack of in-depth academic investigation into its impact on visitors' behavioral intentions regarding museum engagement. This paper employs Cognitive Appraisal Theory, considers human-computer interaction experience as the independent variable, and introduces destination image and satisfaction as mediators to examine their impact on destination loyalty. Based on a survey of 537 participants, the research shows that human-computer interaction experience has a significant positive impact on destination image, satisfaction, and loyalty. Destination image and satisfaction play a partial and sequential mediating role in this relationship. This paper explores the influence mechanism of human-computer interaction experience on destination loyalty and proposes practical interactive solutions for museums, aiming to offer insights for smart tourism research and practice.
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http://dx.doi.org/10.3390/bs15070949 | DOI Listing |
Nat Microbiol
September 2025
Division of Computational Pathology, Brigham and Women's Hospital, Boston, MA, USA.
Although dynamical systems models are a powerful tool for analysing microbial ecosystems, challenges in learning these models from complex microbiome datasets and interpreting their outputs limit use. We introduce the Microbial Dynamical Systems Inference Engine 2 (MDSINE2), a Bayesian method that learns compact and interpretable ecosystems-scale dynamical systems models from microbiome timeseries data. Microbial dynamics are modelled as stochastic processes driven by interaction modules, or groups of microbes with similar interaction structure and responses to perturbations, and additionally, noise characteristics of data are modelled.
View Article and Find Full Text PDFJ Neurosci
September 2025
Institute of Psychology, Leiden University, the Netherlands.
Although phasic alertness generally benefits cognitive performance, it often increases the impact of distracting information, resulting in impaired decision-making and cognitive control. However, it is unclear why phasic alertness has these negative effects. Here, we present a novel, biologically-informed account, according to which phasic alertness generates a transient, evidence-independent input to the decision process.
View Article and Find Full Text PDFJ Med Syst
September 2025
Department of Nursing, ESEP - Porto Higher School of Nursing, Rua Dr. António Bernardino de Almeida, nº 830, Porto, 4200-072, Portugal.
To address the challenges of self-care in oncology, gamification emerges as an innovative strategy to enhance health literacy and self-care among individuals with oncological disease. This study aims to explore and map how gamification can promote health literacy for self-care of oncological diseases. A scoping review was conducted following the Joanna Briggs Institute guidelines and the PRISMA-ScR Checklist developed for scoping reviews.
View Article and Find Full Text PDFPLoS One
September 2025
Department of Otorhinolaryngology-Head and Neck Surgery, Ansan Hospital, Korea University College of Medicine, Ansan, Republic of Korea.
Virtual reality (VR) has been utilized in clinical treatment because it can efficiently simulate situations that are difficult to control in the real world. In this study, we evaluated the efficacy of VR in patients with chronic subjective tinnitus. We assessed the clinical effectiveness based on electroencephalogram (EEG) analysis and questionnaire responses after patients participated in a 6-8-week VR-based tinnitus relief program.
View Article and Find Full Text PDFBrief Bioinform
August 2025
State Key Laboratory of Animal Biotech Breeding, College of Biological Sciences, China Agricultural University, No. 2 Yuanmingyuan West Road, Haidian District, Beijing 100193, China.
The systematic identification of human-virus protein-protein interactions (PPIs) is a critical step toward elucidating the underlying mechanisms of viral infection, directly informing the development of targeted interventions against existing and emerging viral threats. In this work, we presented DeepGNHV, an end-to-end framework that integrated a pretrained protein language model with structural features derived from AlphaFold2 and leveraged graph attention networks to predict human-virus PPIs. In comparison to other state-of-the-art approaches, DeepGNHV exhibited superior predictive performance, especially when applied to viral proteins absent from the training process, indicating its strong generalization capability for detecting newly emerging virus-related PPIs.
View Article and Find Full Text PDF