Different from systems that directly provide online shared courses such as MOOC, online learning systems such as Tencent Classroom simulate a real classroom environment for students and teachers to realize online face-to-face teaching, utilized during the COVID-19 pandemic. Nevertheless, due to the limitation of physical distance, the intelligent design of online learning systems is necessary to provide students with a good learning experience. This study notes that an unexpected optimization effect is the impact of system characteristics on the flow experience of online learning systems, which has not been studied, but plays a vital role in the effectiveness of online learning systems. In the study, a questionnaire was created and multi-stage sampling was used to investigate 623 college students. Based on the DeLone and McLean model of IS success and flow theory, a model for optimizing system characteristics and flow experience was constructed and its effectiveness was tested. The results reveal that system characteristics have a positive impact on continuance intention and flow experience. Additionally, flow experience and learning effect have a positive impact on continuance intention. Furthermore, flow experience has a positive impact on the learning effect. This study emphasizes the flow experience of online learning systems and reveals the optimization direction of online virtual face-to-face classrooms to provide references for the Ministry of Education, schools, and enterprises providing education systems.

Predicting movie box office has received extensive attention from academia and industry. At present, the main method of forecasting movie box office is subjective prediction, which is not widely accepted due to its accuracy and applicability. This study improves the fruit fly algorithm to optimize the generalized regression neural network (IFOA‐GRNN) model to predict whether a movie can become a high‐grossing movie. By using the actual box office data and performing virtual simulation calculations, the root means square error of the IFOA‐GRNN model predicting the movie box office is 0.3412, and the classification accuracy is about 90%. By comparing this model with FOA‐GRNN, KNN, GRNN, Random Forest, Naive Bayes, Ensembles for Boosting, Discriminant Analysis Classifier, and SVM, it is found that the prediction effect of the IFOA‐GRNN model is significantly better than the above eight models. The contribution of this article is to propose a generalized regression neural network model based on an improved fruit fly optimization algorithm, which can greatly improve the accuracy of movie box office prediction.

Light pollution imposes significant and far-reaching adverse effects on human society, necessitating its stringent regulation. However, intervention policies could be customized to suit the unique characteristics of each region, taking into account local conditions. To address this challenge, we have developed a comprehensive light pollution risk assessment model using a combination of objective and subjective weighting methods, including analytic hierarchy process (AHP), independent weighting method (IWM), entropy weight method (EWM), coefficient of variation (CV), criteria importance through intercriteria correlation (CRITIC), and principal component analysis (PCA). This model facilitates a systematic evaluation of light pollution risk levels across diverse regions in China. Subsequently, we have proposed intervention policies targeting light pollution risk reduction and assessed their efficacy using the synthetic control method. Our findings reveal elevated light pollution risk levels in coastal and mountainous regions with heightened concentrations closer to urban centers. Strategies focused on enhancing lighting hardware, optimizing lighting schedules, and upgrading light sources demonstrated the impact on reducing light pollution risk levels (LPRL). This study not only lays a solid theoretical foundation for assessing urban light pollution risks but furnishes empirical evidence to aid relevant authorities in formulating effective light pollution control strategies.

In the digital economy, AI companion platforms integrate upstream AI resources, platform governance, and downstream user engagement, yet sustained emotional interaction does not necessarily translate into paid conversion. To examine this engagement–monetization paradox, this study proposes the Emotion–Barrier–Behavior (EBB) framework as a context-specific integration of relational value, platform frictions, post-adoption continuance, and payment evaluation. A mixed-method design was employed. First, 12,144 online reviews of Xingye, a Chinese AI companion platform, were analyzed through text mining and latent Dirichlet allocation to identify context-specific emotional experiences and usage barriers. Second, structural equation modeling was conducted using survey data from 321 adult respondents. Perceived emotional utility was positively associated with continuance intention but had no significant direct association with willingness to pay; a significant indirect association was observed through continuance intention. Perceived usage barriers were negatively associated with willingness to pay but positively associated with continuance intention and emotional utility in the hypothesized model. However, these barrier-related associations became nonsignificant after controlling for an unmeasured latent method construct and varied across barrier dimensions; they should therefore be interpreted as method-sensitive baseline associations rather than robust structural effects. These cross-sectional findings suggest that continued use and payment intention may diverge in AI companion platforms. The study therefore frames sustainable monetization not as revenue persistence alone, but as commercially viable value capture aligned with SDG 3-related user well-being and SDG 12-related responsible digital consumption and platform practices. These SDG-related outcomes were not directly measured, and broader generalization to AI-driven digital platform ecosystems requires further validation.