Zhejiang Gongshang University

About: Zhejiang Gongshang University is a education organization based out in Hangzhou, China. It is known for research contribution in the topics: Computer science & Chemistry. The organization has 8258 authors who have published 7670 publications receiving 90296 citations. The organization is also known as: Zhèjiāng Gōngshāng Dàxué.

Clinical applications of IDDSI framework for texture recommendation for dysphagia patients.

Abstract: Dysphagia is a highly prevalent eating and swallowing disorder among elderly people, impacting negatively on the health and well-being of those afflicted. With increasing populations of elderly people, food industries are under growing pressure to produce appropriately texture-modified food for safe consumption by these vulnerable populations. Recently published International Dysphagia Diet Standardisation Initiative (IDDSI) framework provides a new global guideline on texture modification and standardization for dysphagia patients. This work was designed to test the feasibility of IDDSI framework for clinical applications by assessing the correlation between swallowing capability of dysphagia patients and the IDDSI texture levels. Altogether 26 elderly subjects were recruited and assessed for their dysphagia grades using the Water Drinking Test. Subjects were provided with fluid samples constituted at different consistencies from a commercial product and swallowing performance (time of swallowing, number of swallows, and number of coughs) was monitored and recorded. Correlations among swallowing capability parameters were observed. Most importantly, results from this work clearly demonstrated that the severity of dysphagia by water-based swallow tests correlates positively with the IDDSI fluid thickness aimed at reducing dysphagia symptoms in those patients, confirming the reliability and feasibility of IDDSI framework for clinical applications. Practical applications Swallowing disorder or dysphagia occurs commonly among many elderly people and imposes negative impacts on their health and well-being. Medical professionals can diagnose eating and swallowing capability in a qualitative manner, but have difficulty in making diet recommendation because of the lack of texture guidance. This work confirmed the feasibility of IDDSI framework for clinical and bedside applications. The correlation between the capability grades of swallowing and IDDSI texture levels established in this work provides a useful measure for such applications.

Fault tolerant channel-encrypting quantum dialogue against collective noise

Abstract: In this paper, two fault tolerant channel-encrypting quantum dialogue (QD) protocols against collective noise are presented. One is against collective-dephasing noise, while the other is against collective-rotation noise. The decoherent-free states, each of which is composed of two physical qubits, act as traveling states combating collective noise. Einstein-Podolsky-Rosen pairs, which play the role of private quantum key, are securely shared between two participants over a collective-noise channel in advance. Through encryption and decryption with private quantum key, the initial state of each traveling two-photon logical qubit is privately shared between two participants. Due to quantum encryption sharing of the initial state of each traveling logical qubit, the issue of information leakage is overcome. The private quantum key can be repeatedly used after rotation as long as the rotation angle is properly chosen, making quantum resource economized. As a result, their information-theoretical efficiency is nearly up to 66.7%. The proposed QD protocols only need single-photon measurements rather than two-photon joint measurements for quantum measurements. Security analysis shows that an eavesdropper cannot obtain anything useful about secret messages during the dialogue process without being discovered. Furthermore, the proposed QD protocols can be implemented with current techniques in experiment.

Industrial structure and economic fluctuation—Evidence from China

Abstract: Industrial structure evolves with economic development. Since the reform and opening up of the economy in 1978, China has undergone rapid economic growth and dramatic industrial restructuring, with...

Achieving Efficient and Privacy-Preserving k -NN Query for Outsourced eHealthcare Data

Abstract: The boom of Internet of Things devices promotes huge volumes of eHealthcare data will be collected and aggregated at eHealthcare provider. With the help of these health data, eHealthcare provider can offer reliable data service (e.g., k-NN query) to doctors for better diagnosis. However, the IT facility in the eHealthcare provider is incompetent with the huge volumes of eHealthcare data, so one popular solution is to deploy a powerful cloud and appoint the cloud to execute the k-NN query service. In this case, since the eHealthcare data are very sensitive yet cloud servers are not fully trusted, directly executing the k-NN query service in the cloud inevitably incurs privacy challenges. Apart from the privacy issues, efficiency issues also need to be taken into consideration because achieving privacy requirement will incur additional computational cost. However, existing focuses on k-NN query do not (fully) consider the data privacy or are inefficient. For instance, the best computational complexity of k-NN query over encrypted eHealthcare data in the cloud is as large as $O(k\log ^{3} N)$ , where N is the total number of data. In this paper, aiming at addressing the privacy and efficiency challenges, we design an efficient and privacy-preserving k-NN query scheme for encrypted outsourced eHealthcare data. Our proposed scheme is characterized by integrating the k d-tree with the homomorphic encryption technique for efficient storing encrypted data in the cloud and processing privacy-preserving k-NN query over encrypted data. Compared with existing works, our proposed scheme is more efficient in terms of privacy-preserving k-NN query. Specifically, our proposed scheme can achieve k-NN computation over encrypted data with $O(lk\log N)$ computational complexity, where l and N respectively denote the data dimension and the total number of data. In addition, detailed security analysis shows that our proposed scheme is really privacy-preserving under our security model and performance evaluation also indicates that our proposed scheme is indeed efficient in terms of computational cost.