The Asia-Pacific region is known as a favorite destination for global medical travelers due to its medical expertise, innovative technology, safety, attractive tourism destination and cost advantage in the recent decade. This study contributes to propose an approach which effectively assesses performance of medical tourism industry based on considering the economic impact factors as well as provides a conceptual framework for the industry analysis. Grey system theory is utilized as a major analyzing approach. According to that, factors impact on the sustainable development of medical tourism in Asia-Pacific region could be identified. The performance of each destination in this region was simultaneously revealed. The results presented an overall perspective of the medical tourism industry in the scope of the Asia-Pacific region, and in Taiwan particularly. Data was collected on six major destinations including Singapore, Thailand, India, South Korea, Malaysia and Taiwan. The results proved that tourism sources and healthcare medical infrastructures play a crucial role in promoting the healthcare travel industry, while cost advantage and marketing effectiveness were less considered. In addition, performance analyse indicated that Thailand has a good performance and stands in the top ranking, followed by Malaysia, India, Singapore, South Korea and Taiwan, respectively. The revenue of Taiwan has increased slowly in the last six years, with a market worth approximately NT$20.5 billion, and the number of medical travelers is expected to increase to 777,523 by 2025. The findings of this study are expected to provide useful information for the medical tourism industry and related key players in strategic planning.

In real practice, forecasting under the limited data has attracted more attention in business activities, especially in the healthcare traveling industry in its current stage. However, there are only a few research studies focusing on this issue. Thus, the purposes of this paper were to determine the forecasted performance of several current forecasting methods as well as to examine their applications. Taking advantage of the small data requirement for model construction, three models including the exponential smoothing model, the Grey model GM(1,1), and the modified Lotka-Volterra model (L.V.), were used to conduct forecasting analyses based on the data of foreign patients from 2001 to 2013 in six destinations. The results indicated that the L.V. model had higher prediction power than the other two models, and it obtained the best forecasting performance with an 89.7% precision rate. In conclusion, the L.V. model is the best model for estimating the market size of the healthcare traveling industry, followed by the GM(1,1) model. The contribution of this study is to offer a useful statistical tool for short-term planning, which can be applied to the healthcare traveling industry in particular, and for other business forecasting under the conditions of limited data in general.

The direct grey model (DGM(2,1)) is considered for fluctuation characteristics of the sampling data in Grey system theory. However, its applications are quite uncommon in the past literature. The improvement of the precision of the DGM(2,1) is only presented in few previous researches. Moreover, the evaluation of forecasted performance of the DGM(2,1) model and its applications was not conducted in previous studies. As the results, this study aims to evaluate forecasted performance of the DGM(2,1) and its three modified models, including the Markov direct grey model MDGM(2,1), the Fourier direct grey model FDGM(2,1), and the Fourier Markov direct grey model FMDGM(2,1) in order to determine the application of the DGM(2,1) model in practical applications and academic research. The results demonstrate that the DGM(2,1) model has lower precision than its modified models, while the forecasted precision of the FDGM(2,1) is better than that of MDGM(2,1). Additionally, the FMDGM(2,1) model presents the best performance among all of the modified models of DGM(2,1), which can effectively overcome the fluctuating of the data sample and minimize the predicted error of the DGM(2,1) model. The finding indicated that the FMDGM(2,1) model does not only have advantages with regard to the sample size requirement, but can also be flexibly applied to the large fluctuation and random sequences with a high quality of estimation.

Electronic paper (e-paper) is a major sector of Taiwan’s Optoelectronic industry. It has paid much attention on the development of flexible displays. Even though the market is booming, the future is still unclear for business development. No research has yet forecasted the future market size of the e-paper industry. In addition, proposing an appropriate forecasting model to understand the trend of this industry plays a crucial role for market players and government’s authorities in formulating correct strategies. Therefore, in this paper, the future market size of Taiwan’s e-paper industry is predicted by an effective combined grey model. Two combinations of DGM(2,1) and Verhulst model with Fourier series and Markov chain, namely FM-Verhulst and FMDGM(2,1), were presented. Based on the annual data of Taiwan’s e-paper industry, the results show that the forecasting performances of two FM-Verhulst and FMDGM(2,1) models are highly accurate compared with other grey models. Precision is 96.36% and 97.77%, respectively. However, for long-term prediction, the FMDGM(2,1) model obtains the best performance in all proposed grey models. With obtained forecasting results in Taiwan’s e-paper industry by the FMDGM(2,1) model, it can be pointed out that the future market size of Taiwan’s e-paper would slowly increase in the next few years.

South Korea (Korea) is a highlight of medical tourism in Asia in the recent years. It has become an attractive destination with the large number of foreign patients around the world. Although, the businesses of medical tourism in this country are quickly expanding, its market size is still unclear due to the shortage of statistical number. Therefore, this study aims to employ forecast tool to estimate the number of foreign patients in Korea in the future. To conduct, the grey forecast has been utilized to construct several forecast models based on the data of foreign patients in Korea from 2009 to 2014. Empirical results indicated that, the GM(1,1) model was presented best forecasted performance with high precision (from 92.1% to 96.7%), compared with the Verhulst model (from 45.5% to 81.3%) and the DGM(2,1) model (from 71.3% to 94.9%). According to the forecasted results of GM(1,1) model, the foreign medical tourists visit to Korea will increase significantly year-on-year. Korea's medical tourism market will attract to 1,378,954 foreign patients in 2020, up to approximately four times from 352,976 in 2015. Among that, to 2020, the number of medical tourists in department of cosmetic and plastic surgeries will achieve 483,615 patients. In addition, for the origin of medical tourist, the number of Chinese and American medical tourist will be estimated to reach 1,163,965 and 82,227 patients respectively in 2020.

Recently, the problem of using efficient the number of mobile devices starting from multi-sink to go to charge and collect data of sensors such that sensors can work forever has received a great deal of attention in Wireless Rechargeable Sensor Network (WRSN). Many methods have been proposed for the WRSN systems such that mobile device can charge and collect data from sensors. However, most of previous works often require lots of mobile devices while the cost of mobile device is very high. In this paper, we investigate the Periodic Energy Replenishment and Data Collection with multiple sink (PERDCMS) problem and propose a new algorithm, called the Mobile Device Scheduling Algorithm (MDSA), to using limited number of mobile devices for charging and collecting data for sensors. Simulation results show that the MDSA has better performance than other method.