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For the healthy development of regional tourism systems, performance evaluation is vital for regulators so that they can determine the source of inefficiency to enhance systems' competitiveness through a series of systematic policy plans. Past research has recognized the importance of network collaboration in the tourism sector but has focused mainly on the separate stages of performance (e.g., hotels), with limited discussion on how sectoral interactions can be distilled into collaborative models. Consequently, this paper intends to construct a more comprehensive performance evaluation framework for the Chinese regional tourism system that not only focuses on network interactions and dynamic features among stages but also considers spatial dependency to enhance the accuracy of performance evaluation. Using the panel data of 30 provincial-administrative regions on China's tourism industry from 2012 to 2016, the operating performance of each regional tourism system and its tourist stages are measured by the SBM-DNDEA model. Furthermore, the spatial effect of regional tourism system operational performance and its influencing factors are investigated via the Tobit spatial Durbin model. The results showed that the operational performance of the Chinese regional tourism system was still relatively low, with the attraction stage performing the best, and there were significant differences among the four economic zones. The strength of traffic convenience had a significant positive effect on the operational performance of the local tourism system, but fiscal expenditure on environmental management also had a negative spillover effect on the surrounding region. The urbanization level had not only a significant positive effect on local areas but also a positive spillover effect on adjacent areas. Interregional development may also affect the performance of attractions, highlighting the importance of systematic integration and allocation of resources for tourism development. [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "Investigating the spatial effect of operational performance in China's regional tourism system" published in Humanities & Social Sciences Communications. The correction addresses an error in one of the author's affiliations and a spelling mistake in the affiliation. The corrected affiliation is now listed as the Accounting School, Nanfang College, Guangzhou, China. The authors of the article are Sheng-Hsiung Chiu, Tzu-Yu Lin, and Wei-Ching Wang. [Extracted from the article]

Ovarian cancer is the deadliest gynaecological disease because of the high mortality rate and there is no any symptom in cancer early stage. It was often the terminal cancer period when patients were diagnosed with ovarian cancer and thus delays a good opportunity of treatment. The current common method for detecting ovarian cancer is blood testing for analyzing the tumor marker CA-125 of serum. However, specificity and sensitivity of CA-125 are insufficient for early detection. Therefore, it has become an urgent issue to look for an efficient method which precisely detects the tumor markers for ovarian cancer. This study aims to find the target genes of ovarian cancer by different algorithms of information science. Feature selection and decision tree were applied to analyze 9600 ovarian cancer-related genes. After screening the target genes, candidate genes will be analyzed by Ingenuity Pathway Analysis (IPA) software to create a genetic pathway model and to understand the interactive relationship in the different pathological stages of ovarian cancer. Finally, this research found 9 oncogenes associated with ovarian cancer and some genes had not been discovered in previous studies. This system will assist medical staffs in diagnosis and treatment at cancer early stage and improve the patient's survival. [ABSTRACT FROM AUTHOR]