Dynamic Characteristic of Consumer Attention in Online Reviews —Empirical Research Based on Mobile Store Reviews

Nowadays consumer online reviews are becoming more and more important for enterprise decision-making. While the existing research seldom discussed review data from a dynamic perspective, especially ignored consumers\u27 attention change during the product life cycle. To study whether there are dynamic changes and the characteristics of changes in the attention degree of consumers in each phase of the product life cycle, this paper coded a specific node program to collect the online reviews data of the four mobile phones in the entire product life cycle and used python\u27s Chinese automatic word segmentation tool library to segment each word and count word frequency, and then a stepwise regression method was used to analyze the dynamic changes of consumer attention. The paper finds that consumers’ attention on logistics and products presented in online reviews show a downward trend, and the attention on brands shows an upward trend; There is no obvious change in the attention degree on services, prices, and promotion; On the different dimensions of products, there is a significant difference in the attention degree. The research results broad the research ideas of online reviews, provide decision-making basis for enterprises to grasp the characteristics of consumers at different stages and to formulate production and marketing strategies

Analysis of the Dilemma and Strategies of Elderly Patients Access to Outpatient Services - Based on the Examples from three Grade A Tertiary Hospitals in Jiangxi Province

Objective: To identify the dilemma of elderly patients' access to outpatient services, develop strategies to improve the environment and functions of the outpatient department, and encourage the elderly to access medical services independently. Methods: By observing and interviewing, this paper studies the environment, behavior, and experiences of elderly patients when accessing medical services, identifies and classifies the key issues, and provides corresponding suggestions. Results: Existing signs and voice prompt systems fail to guide elderly patients to access to medical services; Elderly patients have difficulty in finding places to transit and rest when accessing to outpatient services; Elderly patients have problems in using AI (artificial intelligence) technologies when they access to outpatient services; There are communication barriers between elderly patients and medical staffs. Conclusion: Optimizing the guiding signs and voice prompt systems according to the characteristics of elderly patients; Designing the areas of transition and rest reasonably; Enhancing the ability of elderly patients to use self-service equipment; Promoting the medical treatment process to the elderly in a humanized way

Securities Transaction Tax and Stock Market Behavior in an Agent-based Financial Market Model

AbstractAs highly related to the investors’ earnings expectations and trading decision-making behavior, securities transaction tax (STT) has long been regarded as a typical regulatory mechanism exploited by policy makers. However, neither theoretical analysis nor empirical studies reach consensus about the role and policy effect of the securities transaction tax. Within the framework of agent-based computational finance, this paper presents a new artificial stock market model with heterogeneous agents, which allows us to assess the impacts of varying STTs on market behavior to come to robust conclusions. First we investigate the dynamics of benchmark market with no tax levied, and then market behaviors with different STTs are thoroughly checked. The results show that a modest transactions tax does contribute to stabilize markets by reducing market volatility, but its negative effects on market efficiency cannot be ignored at the same time. The findings suggest that regulatory authorities should introduce STT discreetly to strike a balance between stability and efficiency

Drug Repositioning Based on Bounded Nuclear Norm Regularization

Motivation: Computational drug repositioning is a cost-effective strategy to identify novel indications for existing drugs. Drug repositioning is often modeled as a recommendation system problem. Taking advantage of the known drug–disease associations, the objective of the recommendation system is to identify new treatments by filling out the unknown entries in the drug–disease association matrix, which is known as matrix completion. Underpinned by the fact that common molecular pathways contribute to many different diseases, the recommendation system assumes that the underlying latent factors determining drug–disease associations are highly correlated. In other words, the drug–disease matrix to be completed is low-rank. Accordingly, matrix completion algorithms efficiently constructing low-rank drug–disease matrix approximations consistent with known associations can be of immense help in discovering the novel drug–disease associations. Results: In this article, we propose to use a bounded nuclear norm regularization (BNNR) method to complete the drug–disease matrix under the low-rank assumption. Instead of strictly fitting the known elements, BNNR is designed to tolerate the noisy drug–drug and disease–disease similarities by incorporating a regularization term to balance the approximation error and the rank properties. Moreover, additional constraints are incorporated into BNNR to ensure that all predicted matrix entry values are within the specific interval. BNNR is carried out on an adjacency matrix of a heterogeneous drug–disease network, which integrates the drug–drug, drug–disease and disease–disease networks. It not only makes full use of available drugs, diseases and their association information, but also is capable of dealing with cold start naturally. Our computational results show that BNNR yields higher drug–disease association prediction accuracy than the current state-of-the-art methods. The most significant gain is in prediction precision measured as the fraction of the positive predictions that are truly positive, which is particularly useful in drug design practice. Cases studies also confirm the accuracy and reliability of BNNR. Availability and implementation: The code of BNNR is freely available at https://github.com/BioinformaticsCSU/BNNR. Supplementary information Supplementary data are available at Bioinformatics online