Xuzhou Institute of Technology

About: Xuzhou Institute of Technology is a education organization based out in Xuzhou, China. It is known for research contribution in the topics: Catalysis & Computer science. The organization has 1696 authors who have published 1521 publications receiving 13541 citations.

Carbon quantum dot-based sensors for food safety

Abstract: By taking advantages of the remarkable fluorescent properties, simple preparation strategy, good biocompatibility, and easy functionalization, carbon quantum dots (CQDs) have emerged as promising materials for the application in the sensing, particularly for the food safety. Although great progress has been achieved in recent years, the low selectivity and sensitivity have severely hindered their extensive application as food samples are complex in compositions that may cause interferences. Therefore, more efforts are demanded to contribute to overcoming these issues. Previous researches have demonstrated that the sensing performance of CQDs is strongly correlated with the composites, in which the CQDs that combined with other materials to generate nanostructured composites with distinctive properties could exhibit excellent sensing performance. This article introduces and compares various CQD-based sensors and reviews their related sensing applications as a novel material in food components analysis and food safety inspection, including the fluorescence sensing, electrochemical sensing, and photochemical sensing. It is highly expected to afford a crucial guidance for the rational design of more advanced CQD-based sensors and boosting their applications in food safety.

Medical Image Segmentation Using Fruit Fly Optimization and Density Peaks Clustering.

Abstract: In this paper, we propose a novel algorithm for medical image segmentation, which combines the density peaks clustering (DPC) with the fruit fly optimization algorithm, and it has the following advantages. Firstly, it avoids the problem of DPC that needs to artificially select parameters (such as the number of clusters) in its decision graph and thus can automatically determine their values. Secondly, our algorithm uses random step size, instead of the fixed step size as in the fruit fly optimization algorithm, which helps avoid falling into local optima. Thirdly, our algorithm selects the cut-off distance and the cluster centers using the image entropy value and can better capture the structures of the image. Experiments on benchmark dataset and proprietary dataset show that our algorithm can adaptively segment medical images with faster convergence and better robustness.