期刊：
Neural Computing and Applications,2024年36(17):9849-9874 ISSN：0941-0643
通讯作者：
Liang, L
作者机构：
[Liao, Minglong; Wang, Zongrun; Liang, Lin] Cent South Univ, Business Sch, Changsha 410083, Hunan, Peoples R China.;[Liu, Xin] Hengyang Normal Univ, Coll Econ & Management, Hengyang 421002, Hunan, Peoples R China.;[Liao, Minglong; Liang, Lin] Cent South Univ, Xiangya Hosp, Changsha 410008, Hunan, Peoples R China.
通讯机构：
[Liang, L ] C;Cent South Univ, Business Sch, Changsha 410083, Hunan, Peoples R China.;Cent South Univ, Xiangya Hosp, Changsha 410008, Hunan, Peoples R China.
关键词：
Online health community;Initial trust;Continuous trust;Source of trust;Trust transitivity
摘要：
Internet medical and health services are services that require high levels of trust. We identify factors that influence user trust based on trust source credibility model and trust transitivity model and explore differences in initial and continuous trust formation among users of online health communities from the perspective of trust dynamics. We found that the type of service provision, etc., which represents benevolence trust, whether doctors provide personal photographs, which represents integrity trust, and the overall recommendation popularity, number of electronic gifts, thank you letters, patient votes and positive service quality ratings, which represent trust transitivity, all significantly affect users' initial and continuous trust, but there are differences in the degree of influence on the two types of trust. The doctor's title in the ability trust only has a significant effect on users' initial trust and does not have a significant effect on continuous trust. The three dimensions of ability, benevolence and integrity in the trust source credibility model have a greater impact on users' initial trust than on their continuous trust, while trust transitivity has a greater impact on users' continuous trust than on their initial trust. Overall, in addition to traditional influences such as doctor's title, online information can also support users' decision making, indicating that online health communities can provide useful information to alleviate the current information asymmetry between doctors and patients.
摘要：
A two-step hydrothermal process was used to successfully create Ti3+, N, and B co-doped TiO2 modified with nitrogen doped graphene quantum dots (Ti3+/N/B-TiO2@NGQDs (TNBTN)) composite photocatalyst for photodegradation of bisphenol A (BPA) and methyl orange (MO) under visible-light illumination. The prepared TNBTN composite demonstrated significantly enhanced visible light catalytic performance. Especially, the apparent rate constant of BPA degradation with TNBTN was approximately 56.9 times that of pure TiO2, which was attributed to broader visible light absorbance and faster transmission and separation of photoinduced charge. In addition, the mechanism of BPA and MO degradation with TNBTN was distinct.
摘要：
In the context of public deposit insurance organizational models, several interesting questions arise: Why does China's Deposit Insurance Corporation consistently lean toward the cooperative institution model, which is closely aligned with the central bank? Despite fervent advocacy for the independent institution model by the IADI and the U.S. Why does the unwavering stance exist? Is the choice of the cooperative institution model an "ignorant solution" or an "optimal solution" in China? Our work answers these questions for the first time, and we argue that it is the "optimal solution" that policymakers can choose after careful deliberation, not due to stupidity or inexperience. Based on the Honey Badger Algorithm, real options approach and expected loss pricing model, our work verifies the significant advantages of the cooperative institution model over the independent institution model in China. This pivotal distinction, primarily overlooked in the extant literature, suggests that universally accepted perspectives may not be ubiquitously relevant across all national contexts.
关键词：
long memory;regime switching;FIEGARCH;MRS-FIEGARCH
摘要：
Recent research suggests that long memory can be caused by regime switching and is easily confused with it. However, if the causes of confusion were properly controlled, they could be distinguished. Motivated by this idea, our study aims to distinguish between the long memory and regime switching of financial volatility. We firstly modeled the long memory and regime switching of volatility using the Fractionally Integrated Exponential GARCH (FIEGARCH) and Markov Regime-Switching EGARCH (MRS-EGARCH) frameworks, respectively, and performed a simulation study on their finite-sample properties when innovations followed a non-normal distribution. Subsequently, we demonstrated the confusion between the FIEGARCH and MRS-EGARCH processes using simulations. A recent study theoretically proved that the time-varying smoothing probability series can induce the presence of significant long memory in the regime-switching process. To control for its effect, the two-stage two-state FIEGARCH and MRS-FIEGARCH frameworks are proposed. The Monte Carlo studies showed that both frameworks can effectively distinguish between the pure FIEGARCH and pure MRS-EGARCH processes. When the MRS-FIEGARCH model was further employed to fit series generated with the MRS-FIEGARCH process, it outperformed the ordinary FIEGARCH model. Finally, an empirical study of NASDAQ index return was conducted to demonstrate that our MRS-FIEGARCH model can provide potentially more reliable long-memory estimates, identify the volatility states and outperform both the FIEGARCH and MRS-EGARCH models.
期刊：
IEEE Transactions on Engineering Management,2023年71:6319-6337 ISSN：0018-9391
通讯作者：
Wang, ZR
作者机构：
[Liu, Xin] Hengyang Normal Univ, Coll Econ & Management, Hengyang 421007, Peoples R China.;[Wang, Zongrun; Zhou, Yanju] Cent South Univ, Sch Business, Changsha 410017, Peoples R China.;[Kumar, Ajay] EMLYON Business Sch, F-69130 Ecully, France.;[Biswas, Baidyanath] Trinity Coll Dublin, Trinity Business Sch, Dublin D02 F6N2, Ireland.
通讯机构：
[Wang, ZR ] C;Cent South Univ, Sch Business, Changsha 410017, Peoples R China.
关键词：
Analytical models;Big data analytics;Cognition;Costs;Data mining;data science in healthcare;Diseases;healthcare technology;Medical diagnostic imaging;Medical services;online medicine;PQDR-LDA model;text mining
摘要：
Disease information mining is one of the critical factors affecting users' perception of the disease and has attracted extensive attention from the information management community in recent years. If the mined disease information is incompatible with the disease information perceived by the user, it will eventually lead to the loss of users from the online medical consultation platform, degrading its operation and management. Using existing models to mine disease information leads to significant errors when users perceive the disease. Therefore, this research extends the latent Dirichlet allocation (LDA) and Twitter-LDA models to propose an intelligent topic model, PQDR-LDA. Compared with the Twitter-LDA model, the proposed model has a smaller perplexity value, stronger generalization ability, greater coherence value, lower correlation between topics, and stronger ability in extracting the disease information. It is found that the accuracy of disease diagnosis is very low, and the user's need for perceiving the disease will be reduced while using the traditional model to mine only the text of user questions on an online medical consultation platform. The accuracy of disease diagnosis does not decrease while only mining the doctor's reply text. Disease information that is more suitable for the consultation text can be obtained, which in fact cannot meet the user's real appeal for health, and reduces the users’ needs in perceiving the disease. These findings have important management implications for the platform's operation and decision-making. Besides, users will ask questions in more medical texts simultaneously, which makes things more complicated. Unique management insights are obtained based on the disease information mining of user consultation texts through multiple consultation texts and multiple doctor replies. IEEE
