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.
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.
作者机构：
[Chen, Zhongwen] Hengyang Normal Univ, Coll Econ & Management, Hengyang 421002, Peoples R China.;[Shi, Yanlin] Macquarie Univ, Dept Actuarial Studies & Business Analyt, Sydney, NSW 2109, Australia.;[Shu, Ao] Hunan Univ, Business Sch, Changsha 410012, Peoples R China.
通讯机构：
[Ao Shu] B;Business School, Hunan University, Changsha 410012, China<&wdkj&>Author to whom correspondence should be addressed.
关键词：
mortality rates;Lee–Carter model;time-varying coefficients;rotated age pattern;life expectancy
摘要：
Influential existing research has suggested that rather than being static, mortality declines decelerate at young ages and accelerate at old ages. Without accounting for this feature, the forecast mortality rates of the popular Lee-Carter (LC) model are less reliable in the long run. To provide more accurate mortality forecasting, we introduce a time-varying coefficients extension of the LC model by adopting the effective kernel methods. With two frequently used kernel functions, Epanechnikov (LC-E) and Gaussian (LC-G), we demonstrate that the proposed extension is easy to implement, incorporates the rotating patterns of mortality decline and is straightforwardly extensible to multi-population cases. Using a large sample of 15 countries over 1950-2019, we show that LC-E and LC-G, as well as their multi-population counterparts, can consistently improve the forecasting accuracy of the competing LC and Li-Lee models in both single- and multi-population scenarios.
期刊：
IEEE Transactions on Engineering Management,2023年:1-19 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
