Impact mechanism of social-related social media use on the job performance of caregivers of older adults.

This study investigated the impact mechanism of social-related social media use on the job performance of caregivers of older adults through mental health and analyzed gender differences. A total of 358 valid questionnaires were collected and analyzed using SPSS and Smart PLS. The results showed that mental health plays an important role in the relationship between social-related social media use and job performance. Such social media use can improve psychological well-being and reduce psychological distress by promoting relaxation experience. Psychological well-being has a significant positive impact on job performance. In addition, significant differences exist in the impact path of social-related social media use on psychological distress among different gender groups. The findings of this study can assist nursing homes in assessing the use of social media within their organizations and provide methodological references for enhancing the job performance of caregivers of older adults.

A Method Based on GA-CNN-LSTM for Daily Tourist Flow Prediction at Scenic Spots.

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.

Social Media-Based Health Management Systems and Sustained Health Engagement: TPB Perspective.

Background: With the popularity of mobile Internet and social networks, an increasing number of social media-based health management systems (SocialHMS) have emerged in recent years. These social media-based systems have been widely used in registration, payment, decision-making, chronic diseases management, health information and medical expenses inquiry, etc., and they greatly facilitate the convenience for people to obtain health services. Objective: This study aimed to investigate the factors influencing sustained health engagement of SocialHMS by combining the theory of planned behavior (TPB) with the big-five theory and the trust theory. Method: We completed an empirical analysis based on the 494 pieces of data collected from Anhui Medical University first affiliated hospital (AMU) in East China through structural equation modeling and SmartPLS (statistical analysis software). Results: Openness to new experience has a significantly positive influence on attitude (path coefficient = 0.671, t = 24.0571, R2 = 0.451), perceived behavioral control (path coefficient = 0.752, t = 32.2893, R2 = 0.565), and perceived risk (path coefficient = 0.651, t = 18.5940, R2 = 0.424), respectively. Attitude, perceived behavioral control, subjective norms, and trust have a significantly positive influence on sustained health engagement (path coefficients = 0.206, 0.305, 0.197, 0.183 respectively, t = 3.6684, 4.9158, 4.3414, and 3.3715, respectively). The explained variance of the above factors to the sustained health engagement of SocialHMS is 60.7% (R2 = 0.607). Perceived risk has a significantly negative influence on trust (path coefficient = 0.825, t = 46.9598, R2 = 0.681). Conclusions: Attitude, perceived behavioral control, subjective norm, and trust are the determinants that affect sustained health engagement. The users' personality trait of openness to new experience and perceived risk were also found to be important factors for sustained health engagement. For hospital managers, there is the possibility to take appropriate measures based on users' personality to further enhance the implementation and utilization of SocialHMS. As for system suppliers, they can provide the optimal design for SocialHMS so as to meet users' needs.

[Soil physical properties of different hedgerow systems in upper reaches of Yangtze River].

Based on the investigation of present hedgerows in the upper reaches of Yangtze River, this paper analyzed the soil physical properties at different positions of three kinds of hedgerows (arbor, shrub, and grass). Comparing with those between the hedgerows, the soil physical properties within the hedgerows improved significantly. The average values of soil porosity, moisture content, saturated conductivity, water stable aggregates content, anti-erodibility index, anti-scouribility index, and clay content within the arbor, grass, and shrub hedgerows increased by 18.8%, 30.1%, 12.9%, 139.3%, 108.3%, 95.9%, and 25.5%, and the soil bulk density and sand content averagely decreased by 17.3% and 9.6%, respectively. The soil properties within the three hedgerows differed significantly. The soil anti-scouribility index within arbor hedgerow was the highest; the soil porosity, moisture content, saturated conductivity, anti-scouribility index, water-stable aggregates content, and clay content within the shrub hedgerow were higher than those within the tree and grass hedgerows; while the soil bulk density within the shrub hedgerows was lower than that within the tree and grass hedgerows. Because of the differences in the affecting degree of hedgerow on the soil physical properties at different positions of the three hedgerow systems, the related parameters presented definite horizontal variation at steep lands within, before, and behind the hedgerows, and between the hedgerows. The coefficient of variation (CV) of soil moisture content, anti-erodibility index, saturated conductivity, and clay content of arbor hedgerows was bigger than that of shrub and grass hedgerows, while the CV of soil bulk density, porosity, water-stable aggregates content, and anti-scouribility index of shrub hedgerow was bigger than that of arbor and grass hedgerows.