Chronic hepatitis B poses a serious threat to human health and life, and early diagnosis is essential to improving patient cure rates. Hepatitis B virus (HBV) and Alpha-fetoprotein (AFP) are two key biomarkers for diagnosing chronic hepatitis B. In this study, we propose a silicon nanowire array field effect transistor (SiNW-array FET) biosensor that enables highly sensitive, real-time, and low-cost joint detection of both HBV and AFP. The SiNW-array FET is fabricated using traditional micro-nano fabrication techniques such as self-limiting oxidation and anisotropic etching, and its morphology and electrical properties were tested. The results show that the diameters of the fabricated silicon nanowires (SiNWs) are uniform and the SiNW-array FET exhibits a strong output signal and high signal-to-noise ratio. Through specific chemical modification on the surface of SiNWs, the SiNW-array FET is highly sensitive and specific to HBV-DNA fragments and AFP, with ultralow detection limits of 0.1 fM (HBV-DNA) and 0.1 fg/mL (AFP). The detection curve of the SiNW-array FET exhibits good linearity within the HBV-DNA concentration range of 0.1 fM to 100 pM and AFP concentration range of 0.1 fg/mL to 1000 pg/mL. More importantly, the device could also detect HBV-DNA successfully in serum samples, laying a solid foundation for the highly sensitive clinical detection of chronic hepatitis B.

Image inpainting benefits much from the future Internet, but the memory and computational cost in encoding image features in deep learning methods poses great challenges to this field. In this paper, we propose a parallel decoding structure based on GANs for image inpainting, which comprises a single encoding network and a parallel decoding network. By adding a diet parallel extended-decoder path for semantic inpainting (Diet-PEPSI) unit to the encoder network, we can employ a new rate-adaptive dilated convolutional layer to share the weights to dynamically generate feature maps by the given dilation rate, which can effectively decrease the number of convolutional layer parameters. For the decoding network composed of rough paths and inpainting paths, we propose the use of an improved CAM for reconstruction in the decoder that results in a smooth transition at the border of defective areas. For the discriminator, we substitute the local discriminator with a region ensemble discriminator, which can attack the restraint of only the recovering square, like areas for traditional methods with the robust training of a new loss function. The experiments on CelebA and CelebA-HQ verify the significance of the proposed method regarding both resource overhead and recovery performance.

With the extensive use of peer-to-peer applications in recent years, the network traffic becomes more dynamic and less predictable, which leads to the decline of network resource utilization and the degradation of network performance. Aiming towards the above problems, we explore how to strengthen the cooperation between peer-to-peer applications and networks, making the application adjust its own traffic mode according to current network traffic status to enhance the stability of network traffic. We improve two key algorithms of peer selection and choking/unchoking in the protocol and introduce traffic relaxation to characterize traffic state while taking the current most popular peer-to-peer application (bit torrent protocol) as an example. In our improved method, peers are selected probabilistically according their traffic relaxation, and the double-parameter selection problem that simultaneously considers the traffic relaxation and transfer rate of peers is also solved. Finally, we conduct simulation experiments in two real network typologies with real traffic matrix data and different sizes of bit torrent swarms; the experimental results show that our method can significantly improve the stability of the network traffic without sacrificing or even improving the performance of the bit torrent protocol when compared with original BT protocol.

Pubertal timing and social adaptability are important research contents of adolescent mental health education. Traditional research methods mainly classify students based on the total score or average score of the scale, although this kind of method is simple easy to conduct, it can't make a more detailed analysis of the students. In this paper, data mining methods such as association rules and clustering are used to analyze the data of pubertal timing and social adaptability scale, some novel and meaningful conclusions are figured out from the analysis results that can't be obtained by the previous methods, and the analysis results are visualized to enhance readability. Association rule mining on basic attributes information, the pubertal timing group and the social adaptability levels were performed which can explore the relationship between the basic attributes information of the students, pubertal timing and the social adaptability. Fine-grained analysis of social adaptability by using clustering method was conducted which can divide the similar students into the same groups that is very useful for teachers to have a more in-depth, accurate and detailed understanding of students, make sure that the better classification can be obtained compared with the traditional analysis approaches. The work of this paper provides an effective guidance and a novel perspective for how to use data mining technologies to study the pubertal timing and social adaptability problems.