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Actuality and Development of the Tourism online services in China

Chinese Business Review 2005年第8期4卷 43-48,53页

作者： Changfu Wang Min Wei Xi＇an Jiaotong University/Hunan Polytechnical University Xiamen University

The Chinese tourism industry has improved largely over last few years, which is known as the Golden Tourism Project . This paper discusses this project by investigating online tourism service development in China, which are the tourism websites, the tourism website users and the tourism website providers. This paper begins the reality of China s tourism online service, and the results can be got at last, which show that the majority of tourism website providers are regional tourism destination organizations that mainly provide comprehensive local tourism information and online services. In conclusion, this paper proposes possible approaches to improve the quality of online tourism services in China.

关键词： online services China Tourism

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E- Governance: Taipei City Government online services Change in Administrative Processes

E- Governance: Taipei City Government Online Services Change...

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2006 International Conference on Public Administration

作者： Shih-Hsien CHANG Dept. of Public Administration and Policy National Taipei University

Providing government online services can be considered as a kind of citizen-oriented government reform. Information-technology based government online services affect traditional administrative flow, coordination, and the nature of administrative word by altering the working relationship between information and the physical factors of distance, time, and memory. These properties of online services allow government to change the way of providing services to citizens. By using comparative analysis with 13 comparative items, the paper mainly studies how and what several important changes of Taipei’s online services from the old to the new administrative process.

关键词： online services e-governance Government administration "informationization"

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Predicting protein subchloroplast locations:the 10th anniversary

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Frontiers of Computer Science 2021年第2期15卷 1-11页

作者： Jian SUN Pu-Feng DU College of Intelligence and Computing Tianjin UniversityTianjin300350China

Chloroplast is a type of subcellular organelle in green plants and *** is the main subcellular organelle for conducting photosynthetic *** proteins,which localize within the chloroplast,are responsible for the photosynthetic process at molecular *** chloroplast can be further divided into several *** in different compartments are related to different steps in the photosynthetic *** the molecular function of a protein is highly correlated to the exact cellular localization,pinpointing the subchloroplast location of a chloroplast protein is an important step towards the understanding of its role in the photosynthetic *** process for determining protein subchloroplast location is always costly and time ***,computational approaches were developed to predict the protein subchloroplast locations from the primary *** the last decades,more than a dozen studies have tried to predict protein subchloroplast locations with machine learning *** sequence features and various machine learning algorithms have been introduced in this research *** this review,we collected the comprehensive information of all existing studies regarding the prediction of protein subchloroplast *** compare these studies in the aspects of benchmarking datasets,sequence features,machine learning algorithms,predictive performances,and the implementation *** summarized the progress and current status in this special research *** also try to figure out the most possible future works in predicting protein subchloroplast *** hope this review not only list all existing works,but also serve the readers as a useful resource for quickly grasping the big picture of this research *** also hope this review work can be a starting point of future methodology studies regarding the prediction of protein subchloroplast locations.

关键词： subchloroplast locations sequence features performance measures online services machine learning

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