Online health communities allow doctors to fully use existing medical resources to serve remote patients. They broaden and diversify avenues of interaction between doctors and patients using Internet technology, which have built an online medical consultation market. In this study, the theory of supply and demand was adopted to explore how market conditions of online doctor resources impact price premiums of doctors' online service. Then, we investigated the effect of the stigmatized diseases. We used resource supply and resource concentration to characterize the market conditions of online doctor resources and a dummy variable to categorize whether the disease is stigmatized or ordinary. After an empirical study of the dataset (including 68,945 doctors), the results indicate that: (1) the supply of online doctor resources has a significant and negative influence on price premiums; (2) compared with ordinary diseases, doctors treating stigmatized diseases can charge higher price premiums; (3) stigmatized diseases positively moderate the relationship between resource supply and price premiums; and (4) the concentration of online doctor resources has no significant influence on price premiums. Our research demonstrates that both the market conditions of online doctor resources and stigmatized diseases can impact price premiums in the online medical consultation market. The findings provide some new and insightful implications for theory and practice. returns for doctors, decreasing health disparities for society as a whole, in addition to providing other benefits [5,[9][10][11]. Currently, many online health communities provide similar online services for doctors and patients, such as Good Doctor and Chunyu Doctor in China, and Quickrxrefill, MDproactive, and Livehealthonline in the USA.Compared with doctors in physical hospitals, in online health communities, doctors have more options to choose service provision at their convenience [12]. Online medical consultation, as one of the most popular and useful service functions, is provided for patients who need professional medical diagnosis and do not visit hospitals [13]. Doctors in online health communities have the pricing power for the online medical consultation service [12]; they can set price for the online medical consultation service based on their capabilities and circumstances [14,15]. Many studies have examined factors affecting service price and returns [11,12,16], such as doctor reputation, professional title, and hospital rank. Recent studies have mainly focused on the relationships between service prices and doctors' personal characteristics, which is necessary but insufficient. Online health communities have built an online medical consultation marketplace for doctors and patients [17,18]. In this online market, doctors with strong medical skills can obtain high service fees above the average price, to charge price premiums and receive higher returns [19,20]. In our study, the research context, Good Doctor (Haodf.com), is one of the leading and typica...
As one of the most important grain protection policies in China, the minimum purchase price policy prevents the fluctuation of grain output and protects the interests of farmers by regulating the prices of major grain varieties. For developing countries with a shortage of agricultural resources, represented by China, an in-depth study on the implementation effect and public satisfaction of this policy is of great significance for promoting the sustainable development of the grain industry. Based on the interest demands of the government, farmers, grain enterprises and consumers, this paper constructs a policy satisfaction evaluation model based on the Analytic Hierarchy Process and Fuzzy Comprehensive Evaluation. The research shows that the implementation effect of this policy has promoted the sustainable development of China’s grain in four aspects: improving farmers’ enthusiasm for planting, optimizing the structure of supply and demand, reducing the adverse impact of disasters, and ensuring the steady increase of output. However, due to the differences in natural resources and folk customs, the implementation effect of this policy varies in different regions.
This report discusses the application of approximate dynamic programming (ADP) and Krylov subspace methods in solving sequential decision-making problems in machine learning. ADP is used when dealing with large state spaces or when the exact dynamics are unknown. The paper explores various ADP methods such as temporal difference learning and least-squares policy evaluation. The authors also focus on the use of Krylov subspace methods for solving the Bellman equation in ADP. The report provides insights into linear approximation, stochastic algorithms, and the Arnoldi algorithm for orthonormal basis. Overall, the paper highlights the theoretical foundation provided by ADP and its connection with Krylov subspace methods, shedding light on their application in computer science.
As people's consumption habits change, loan plays a crucial role in our modern society. It provides individuals who do not have sufficient money with funds to purchase residential property or start a business. However, for avoiding unpleasant loan defaults, all financial institutions will first assess the borrower's risk index. By predicting the default risk of the borrower to decide whether to lend money. Machine learning algorithms, including random forest, linear regression and so on, have been benefited most of the real-world applications. With the development of machine learning methods, this paper, based on the personal history loan data of an institution studies the loan default risk, and uses the random forest classification model to predict the possibility of loan default. The result showed that the accuracy of this method was 85.62%, which show its application ability of real-world loan prediction and benefits the manager to decide the degree of risk for loan grant.
-With the process of the construction of human ecological civilization, low-carbon economy, low-carbon technology, carbon-sink mechanism and low-carbon consumptive life style are increasingly affecting and inducing human productive and consumptive patterns. Tourism, as a product of human progress for civilization, has congenital advantage of responding low-carbon economic model, utilizing low-carbon technology, pursuing lowcarbon mechanism and advocating low-carbon consumptive pattern. This paper discusses the theoretical basis for the development of lowcarbon tourism, analyzes the main action bodies and their actions of low-carbon travel based on the low-carbon tourism development model. Last we think that the way of achieving the low-carbon tourism is that the government, tourist company and travelers should interact each othe, and fulfill the low-carbon sustainable development.
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