Blended learning, is a teaching approach that integrates online self-learning and classroom teaching. When designed well, blended learning courses in medicine can facilitate students to improve themselves in self-learning, understanding, and problem solving, ultimately enhancing their learning efficiency. However, blended teaching methods are usually used in only a single course, so it is unclear whether these methods can work well in a variety of basic medical courses. The goal of this study is to explore students’ perceptions of whether blended laboratory courses are helpful for them in overcoming the difficulties they experience. Blended laboratory courses were taken by medical students at Guilin Medical University. Approximately 71.1% of the students agreed that online lecture courses improved their understanding of threshold concepts and the underlying theories. The majority of the students (63.01%) held the opinion that the blended laboratory courses were more effective than other types of courses in achieving the knowledge goals. The majority of the teachers believed that students’ interest in experimentation operations, hands-on abilities, confidence, and other factors were greatly improved compared with those of students taught using the traditional teaching model (face to face). In addition, the average scores for the quizzes of laboratory courses were significantly improved in the blended learning method compared with the traditional learning method. Blended laboratory courses are successful and welcomed by both students and teachers in undergraduate laboratory courses.

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At present, the energy structure of China is shifting towards cleaner and lower amounts of carbon fuel, driven by environmental needs and technological advances. Nuclear energy, which is one of the major low-carbon resources, plays a key role in China's clean energy development. To formulate appropriate energy policies, it is necessary to conduct reliable forecasts. This paper discusses the nuclear energy consumption of China by means of a novel fractional grey model FAGMO(1,1,k). The fractional accumulated generating matrix is introduced to analyse the fractional grey model properties. Thereafter, the modelling procedures of the FAGMO(1,1,k) are presented in detail, along with the transforms of its optimal parameters. A stochastic testing scheme is provided to validate the accuracy and properties of the optimal parameters of the FAGMO (1,1,k). Finally, this model is used to forecast China's nuclear energy consumption and the results demonstrate that the FAGMO(1,1,k) model provides accurate prediction, outperforming other grey models.

This explorative paper considers the recent developments in the emerging small family business sector in post-reform China as the country embraces socio-economic and structural transition from a centrally planned to a market-orientated system. The important contributions that Chinese small family firms play in the acceleration of private sector development across the social and industrial sectors as well as the geographic boundaries of the Pacific Rim are highlighted. The authors propose typologies of Chinese entrepreneurship and tentative enterprise policy recommendations for the future development of small private family businesses in China

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Abstract-The security of the two party Diffie-Hellman key exchange protocol is currently based on the discrete logarithm problem (DLP). However, it can also be built upon the elliptic curve discrete logarithm problem (ECDLP). Most proposed secure group communication schemes employ the DLP-based Diffie-Hellman protocol. This paper proposes the ECDLP-based Diffie-Hellman protocols for secure group communication and evaluates their performance on wireless ad hoc networks. The proposed schemes are compared at the same security level with DLP-based group protocols under different channel conditions. Our experiments and analysis show that the Tree-based Group Elliptic Curve Diffie-Hellman (TGECDH) protocol is the best in overall performance for secure group communication among the four schemes discussed in the paper. Low communication overhead, relatively low computation load and short packets are the main reasons for the good performance of the TGECDH protocol.

With the tremendous growth of smart mobile devices, the Content-Based Image Retrieval (CBIR) becomes popular and has great market potentials. Secure image retrieval has attracted considerable interests recently due to users' security concerns. However, it still suffers from the challenges of relieving mobile devices of excessive computation burdens, such as data encryption, feature extraction, and image similarity scoring. In this paper, we propose and implement an IND-CPA secure CBIR framework that performs image retrieval on the cloud without the user's constant interaction. A pre-trained deep CNN model, i.e., VGG-16, is used to extract the deep features of an image. The information about the neural network is strictly concealed by utilizing the lattice-based homomorphic scheme. We implement a real number computation mechanism and a divide-and-conquer CNN evaluation protocol to enable our framework to securely and efficiently evaluate the deep CNN with a large number of inputs. We further propose a secure image similarity scoring protocol, which enables the cloud servers to compare two images without knowing any information about their deep features. The comprehensive experimental results show that our framework is efficient and accurate.INDEX TERMS Content-based image retrieval, convolutional neural network (CNN), lattice-based homomorphic scheme.

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