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The treatment of diabetes lies in developing novel functional carriers, which are expected to have the unique capability of monitoring blood glucose levels continuously and dispensing insulin correctly and timely. Hence, this study is proposing to create a smart self-regulated insulin delivery system according to changes in glucose concentration. Temperature and glucose dual responsive copolymer microcapsules bearing N-isopropylacrylamide and 3-acrylamidophenylboronic acid as main components were developed by bottom-spray coating technology and template method. The insulinoma β-TC6 cells were trapped in the copolymer microcapsules by use of temperature sensitivity, and then growth, proliferation, and glucose-responsive insulin secretion of microencapsulated cells were successively monitored. The copolymer microcapsules showed favorable structural stability and good biocompatibility against β-TC6 cells. Compared with free cells, the biomicrocapsules presented a more effective and safer glucose-dependent insulin release behavior. The bioactivity of secreted and released insulin did not differ between free and encapsulated β-TC6 cells. The results demonstrated that the copolymer microcapsules had a positive effect on real-time sensing of glucose and precise controlled release of insulin. The intelligent drug delivery system is supposed to mimic insulin secretion in a physiological manner, and further provide new perspectives and technical support for the development of artificial pancreas. [ABSTRACT FROM AUTHOR]

Saliency prediction has recently gained a large number of attention for the sake of the rapid development of deep neural networks in computer vision tasks. However, there are still dilemmas that need to be addressed. In this paper, we design a visual saliency prediction model using attention-based cross-model integration strategies in RGB-D images. Unlike other symmetric feature extraction networks, we exploit asymmetric networks to effectively extract depth features as the complementary information of RGB information. Then we propose attention modules to integrate cross-modal feature information and emphasize the feature representation of salient regions, meanwhile neglect the surrounding unimportant pixels, so as to reduce the lost of channel details during the feature extraction. Moreover, we contribute successive dilated convolution modules to reduce training parameters and to attain multi-scale reception fields by using dilated convolution layers, also, the successive dilated convolution modules can promote the interaction of two complementary information. Finally, we build the decoder process to explore the continuity and attributes of different levels of enhanced features by gradually concatenating outputs of proposed modules and obtaining final high-quality saliency prediction maps. Experimental results on two widely-agreed datasets demonstrate that our model outperforms than other six state-of-the-art saliency models according to four measure metrics. [ABSTRACT FROM AUTHOR]

W ith the rapid expansion of data scale, big data mining and analytics has attracted increasing attention. Outlier detection as an important task of data mining is widely used in many applications. However, conventional outlier detection methods begin to have difficulty handling large datasets. In addition, most existing outlier detection methods typically can only identify global outliers and are over sensitive to parameters variation. In this paper, we propose a novel method for robust local outlier detection with statistical parameter which incorporates the new ideas in dealing with big data. This method not only can effectively identify both global and local outliers but also is associated with only one statistical parameter. Furthermore, the sole parameter can be easily determined with out relying on users' domain knowledge. Most importantly, the method is insensitive to parameter variation, which ensures its superiority in robustness. The properties of the method are investigated and the performance is experimentally verified using synthetic and publicly available datasets. The experiments demonstrate the accuracy and efficiency of the method in identifying local outliers. Moreover, the method is also proved more robust to parameter variation than the well-known local outlier detection method LOF and two other representative outlier detection methods, DB and DBSCAN. The results show that the proposed method has superiority in handling big data. [ABSTRACT FROM AUTHOR]

With the distributed sensors that are deployed to monitor the urban environment in smart cities, the sensed data are overwhelming and beyond the scope of the wireless sensor networks (WSNs) capability. Due to the communication range limits of the sensors, most of these data will be outsourced and stored on some untrusted servers. Thus, how to maintain the data confidentiality and integrity, as well as source authentication and data query privacy of the outsourced data, is a challenging problem. In this paper, we propose a secure searchable encryption scheme, named SSE, for urban sensing and querying to address the problem. Specifically, our SSE constructs a secure hidden vector encryption-(HVE-) based rang query predicate. The sensed data can be stored on an untrusted server in encrypted form. A requester can obtain the correct ciphertexts when his authorized range query matches the HVE-based encryption predicate. With the help of the base station, the ciphertexts can be decrypted and data integrity can be verified; then, the requester can obtain the correct original data. Security analysis demonstrates that; in the SSE, only the authorized requesters can obtain the query results, while the data confidentiality and integrity and source authentication are also preserved. [ABSTRACT FROM AUTHOR]