Entrepreneurial activities of college students have received unprecedented attention under the “mass entrepreneurship and innovation” program. This program encourages individuals to start their own businesses and stimulate the entrepreneurial spirit and innovative genes of the nation. Sustainable entrepreneurship is a new form of entrepreneurship that plays an important role in addressing the employment problems of college students, promoting sustainable social and economic development, and alleviating China’s environmental problems. Based on the platform of the China College Students’ “Internet Plus” Innovation and Entrepreneurship Competition (CSIPC) organized by the Ministry of Education of People’s Republic of China, we investigated the specific paths of personality trait influence on sustainable entrepreneurial intention of college students with entrepreneurial alertness and opportunity recognition as mediating variables. We conducted an empirical analysis based on 316 data collected from CSIPC participants. Results of data analysis show that personality traits had a significantly effect on sustainable entrepreneurial intention of college students, and entrepreneurial alertness and opportunity recognition played a mediating role between personality traits and sustainable entrepreneurial intention of college students. This study contributes to research on sustainable entrepreneurship and the practices of colleges to foster sustainable entrepreneurs.

Accurate tourist flow prediction is key to ensuring the normal operation of popular scenic spots. However, one single model cannot effectively grasp the characteristics of the data and make accurate predictions because of the strong nonlinear characteristics of daily tourist flow data. Accordingly, this study predicts daily tourist flow in Huangshan Scenic Spot in China. A prediction method (GA-CNN-LSTM) which combines convolutional neural network (CNN) and long-short-term memory network (LSTM) and optimized by genetic algorithm (GA) is established. First, network search data, meteorological data, and other data are constructed into continuous feature maps. Then, feature vectors are extracted by convolutional neural network (CNN). Finally, the feature vectors are input into long-short-term memory network (LSTM) in time series for prediction. Moreover, GA is used to scientifically select the number of neurons in the CNN-LSTM model. Data is preprocessed and normalized before prediction. The accuracy of GA-CNN-LSTM is evaluated using mean absolute percentage error (MAPE), mean absolute error (MAE), Pearson correlation coefficient and index of agreement (IA). For a fair comparison, GA-CNN-LSTM model is compared with CNN-LSTM, LSTM, CNN and the back propagation neural network (BP). The experimental results show that GA-CNN-LSTM model is approximately 8.22% higher than CNN-LSTM on the performance of MAPE.

The accurate prediction of tourist flow is essential to appropriately prepare tourist attractions and inform the decisions of tourism companies. However, tourist flow in scenic spots is a dynamic trend with daily changes, and specialized methods are necessary to measure it accurately. For this purpose, a tourist flow forecasting method is proposed in this research based on seasonal clustering. The experiment employs the K-means algorithm considering seasonal variations and the particle swarm optimization-least squares support vector machine (PSO-LSSVM) algorithm to forecast the tourist flow in scenic spots. The LSSVM is also used to compare the performance of the proposed model with that of the existing ones. Experiments based on a dataset comprising the daily tourist data for Mountain Huangshan during the period between 2014 and 2017 are conducted. Our results show that seasonal clustering is an effective method to improve tourist flow prediction, besides, the accuracy of daily tourist flow prediction is significantly improved by nearly 3 percent based on the hybrid optimized model combining seasonal clustering. Compared with other algorithms which provide predictions at monthly intervals, the method proposed in this research can provide more timely analysis and guide professionals in the tourism industry towards better daily management.

The power Heronian aggregation (PHA) operator can use the advantages of power average and the Heronian mean operator, which together could take into account the interrelationship of the aggregated arguments, and therefore alleviate the effects caused by unreasonable data through considering the support degree between input arguments. However, PHA operators cannot be used to process single-valued neutrosophic numbers (SVNNs), which is significant for extending it to SVNNs. We propose some new PHA operators for SVNNs and introduce a novel MAGDM method on the basis of the proposed operators. Firstly, the definition, properties, comparison method, and operational rules of SVNNs are introduced briefly. Then, some PHA operators are proposed, such as the single-valued neutrosophic power Heronian aggregation (SVNPHA) operator, the single-valued neutrosophic weighted power Heronian aggregation (SVNWPHA) operator, single-valued neutrosophic geometric power Heronian aggregation (SVNGPHA) operator, single-valued neutrosophic weighted geometric power Heronian aggregation (SVNWGPHA) operator. Furthermore, we discuss some properties of these new aggregation operators and several special cases. Moreover, the method to solve the MAGDM problems with SVNNs is proposed, based on the SVNWPHA and SVNWGPHA operators. Lastly, we verified the application and effectiveness of the proposed method by using an example for the MAGDM problem.

Accurate tourist flow forecasting is an important issue in tourist destinations management. Given the influence of various factors on varying degrees, tourist flow with strong nonlinear characteristics is difficult to forecast accurately. In this study, a deep learning method, namely, Gated Recurrent Unit (GRU) is used for the first time for tourist flow forecasting. GRU captures long-term dependencies efficiently. However, GRU’s ability to pay attention to the characteristics of sub-windows within different related factors is insufficient. Therefore, this study proposes an improved attention mechanism with a horizontal weighting method based on related factors importance. This improved attention mechanism is introduced to the encoding–decoding framework and combined with GRU. A competitive random search is also used to generate the optimal parameter combination at the attention layer. In addition, we validate the application of web search index and climate comfort in prediction. This study utilizes the tourist flow of the famous Huangshan Scenic Area in China as the research subject. Experimental results show that compared with other basic models, the proposed Improved Attention-based Gated Recurrent Unit (IA-GRU) model that includes web search index and climate comfort has better prediction abilities that can provide a more reliable basis for tourist destinations management.

Older people with hearing impairment are more likely to develop depressive symptoms due to physical disability and loss of social communication. This study investigated the effects of social media on social relations, subjective aging, and depressive symptoms in these older adults based on the stimulus-organism-response (S-O-R) framework. It provides new empirical evidence to support improving the mental health and rebuilding the social relations of older people. A formal questionnaire was designed using the Wenjuanxing platform and distributed online through WeChat; 643 valid questionnaires were received from older people with self-reported hearing impairments, and SmartPLS 3.28 was used to analyze the data. The results show that (1) social media significantly impacts the social relations of older people with hearing impairment (social networks, β = 0.132, T = 3.444; social support, β = 0.129, T = 2.95; social isolation, β = 0.107, T = 2.505). (2) For these older people, social isolation has the biggest impact on their psychosocial loss (β = 0.456, T = 10.458), followed by the impact of social support (β = 0.103, T = 2.014); a hypothesis about social network size was not confirmed (β = 0.007, T = 0.182). Both social media (β = 0.096, T = 2.249) and social support (β = 0.174, T = 4.434) significantly affect the self-efficacy of hearing-impaired older people. (3) Both subjective aging (psychosocial loss, β = 0.260, T = 6.036; self-efficacy, β = 0.106, T = 3.15) and social isolation (β = 0.268, T = 6.307) significantly affect depressive symptoms in older people with hearing impairment. This study expands the theories of social media aging cognition, social support, and social networks and can provide practical contributions to the social media use and mental health of special persons 60 years and older.