Background: The increasing aging population has led to a shortage of geriatric chronic disease caregiver, resulting in inadequate care for elderly people. In this global context, many older people rely on nonprofessional family care. The credibility of existing health websites cannot meet the needs of care. Specialized health knowledge bases such as SNOMED—CT and UMLS are also difficult for nonprofessionals to use. Furthermore, professional caregiver in elderly care institutions also face difficulty caring for multiple elderly people at the same time and working handovers. As a solution, we propose a smart care system for the elderly based on a knowledge graph. Method: First, we worked with professional caregivers to design a structured questionnaire to collect more than 100 pieces of care-related information for the elderly. Then, in the proposed system, personal information, smart device data, medical knowledge, and nursing knowledge are collected and organized into a dynamic knowledge graph. The system offers report generation, question answering, risk identification and data updating services. To evaluate the effectiveness of the system, we use the expert evaluation method to score the user experience. Results: The results of the study showed that compared to existing tools (health websites, archives and expert team consultation), the system achieved a score of 8 or more for basic information, health support and Dietary information. Some secondary evaluation indicators reached 9 and 10 points. This finding suggested that the system is superior to existing tools. We also present a case study to help the reader understand the role of the system. Conclusion: The smart care system provide personalized care guidelines for nonprofessional caregivers. It also makes the job easier for institutional caregivers. In addition, the system provides great convenience for work handover.

As the financial market of China continues to grow, how to control the potential risks in the financial market and to maintain the bottom line without large systemic financial risks becomes a problem that must be faced. The VaR method has become the most widely used financial risk measurement method by its simplicity, flexibility and flexibility. Based on the data of the CSI 300 Index in the past five years, this paper uses Monte Carlo simulation and historical simulation to calculate the VaR of the five-year index and test its validity. Finally, combining with the characteristics of China’s market economy, it puts forward some suggestions on financial risk management in China’s financial market.

This study investigated different patterns of emotional labor among community nurses in China and analyzed the relationships between the sense of career success and emotional labor. A total of 385 community nurses from Beijing participated in this investigation. Latent class analysis was used to identify meaningful subgroups of participants, and analysis of variance was used to analyze relationships between emotional labor classes and the sense of career success. Emotional labor among community nursing staff in China was divided into three latent classes: active (n = 90, 25.6%), apathetic (n = 65, 18.5%), and moderate (n = 197, 55.9%). The active emotional labor classes had significantly higher career success (p<0.05). The “gaining recognition” dimension showed significant differences across the three classes. Our findings suggested managers to implement a variety of measures to strengthen interventions for employees’ emotional labor that are targeted to incentive mechanisms, which will improve nurses’ sense of career success.

Due to external factors such as political influences, specific events and sentiment information, stock prices exhibit randomness, high volatility and non-linear characteristics, making accurate predictions of future stock prices based solely on historical stock price data difficult. Consequently, data fusion methods have been increasingly applied to stock price prediction to extract comprehensive stock-related information by integrating multi-source heterogeneous stock data and fusing multiple decision results. Although data fusion plays a crucial role in stock price prediction, its application in this field lacks comprehensive and systematic summaries. Therefore, this paper explores the theoretical models used in each level of data fusion (data-level, feature-level and decision-level fusion) to review the development of stock price prediction from a data fusion perspective and provide an overall view. The research indicates that data fusion methods have been widely and effectively used in the field of stock price prediction. Additionally, future directions are proposed. For better performance of data fusion in the field of stock price prediction, future work can broaden the scope of stock-related data types used and explore new algorithms such as natural language processing (NLP) and generative adversarial networks (GAN) for text information processing.