In the current era, the rapid spread of Internet technology has combined with various traditional industries; this provides new research perspectives and solutions for current problems, such as those in the elderly care industry. Elderly health is an important social problem in various countries, and governments have turned to the internet for new methods and better solutions. However, internet-use behavior has a certain influence on the elderly’s health status. This study investigates the effects of internet use on the elderly’s physical health, mental health, and self-rated health, along with the moderating role of individual cognitive ability in the above relationship. This study uses data from the Chinese General Social Survey (CGSS) in 2012 and 2015 as samples for analysis via the hierarchical regression method. The sample is from China and had 2821 and 3185 valid respondents in 2012 and 2015, respectively. Results show that internet use significantly affects the physical and mental health of the elderly and does not significantly affect self-rated health. In addition, individual cognitive ability plays a negative moderating role between internet use and physical and mental health. Finally, on the basis of results analysis and discussion, this study provides new recommendations to achieve targeted health improvements.

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 Chinese tourism industry has been developing rapidly for the past several years, and the number of people traveling has been increasing year by year. However, many problems still beset current tourism management. Lack of effective management has caused numerous problems, such as tourists stranded during tourist season and the declining service quality of scenic spots, which have become the focus of tourists’ attention. Network search data can intuitively reflect the attention of most users through the combination of the network search index and the back propagation (BP) neural network model. This study predicts the daily tourism demand in the Huangshan scenic spot in China. The filtered keyword in the Baidu index is added to the hybrid neural network, and a BP neural network model optimized by a fruit fly optimization algorithm (FOA) based on the web search data is established in this study. Different forecasting methods are compared in this paper; the results prove that compared with other prediction models, higher accuracy can be obtained when it comes to the peak season using the FOA-BP method that includes web search data, which is a sustainable means of practically solving the tourism management problem by a more accurate prediction of tourism demand of scenic spots.

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.

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.

Purpose: Blockchain technology has been recognized as a potential solution to the challenges in managing healthcare information. Its adoption in the healthcare industry has garnered the attention of healthcare institutions and governments. Given the significant role of subsidies in promoting technology adoption, this study applies evolutionary game theory to examine the impact of government subsidies on the adoption of blockchain technology by healthcare institutions.Design/methodology/approach: First, the authors analyze the interests of government administration departments and healthcare institutions separately in regards to blockchain adoption. Subsequently, the authors develop the payoff matrix of both participants and construct the evolutionary game model. And then, the authors calculate the replication dynamic equations and analyze the decision evolution of both participants through the replication dynamic equations and numerical experiments.Findings: The numerical experiments demonstrate that government subsidies are effective in encouraging healthcare institutions to adopt blockchain technology. The study also reveals the necessary amount of subsidy required to guide healthcare institutions towards adoption. Additionally, the validity of the evolutionary game model in analyzing the interaction between governments and healthcare institutions is confirmed by the results.Originality/value: Blockchain adoption in the healthcare industry differs from other emerging technologies, as there is the potential for it to reduce revenue for healthcare institutions. This study contributes to the analysis of theoretical models for promoting blockchain in the healthcare industry through subsidies. Additionally, it demonstrates the potential of evolutionary game theory in analyzing the adoption of blockchain technology, and the interaction between governments and healthcare institutions.