Shandong Normal University

About: Shandong Normal University is a education organization based out in Jinan, Shandong, China. It is known for research contribution in the topics: Laser & Catalysis. The organization has 12378 authors who have published 12576 publications receiving 174572 citations.

Asymmetric signal amplification for simultaneous SERS detection of multiple cancer markers with significantly different levels.

Abstract: Simultaneous detection of cancer biomarkers holds great promise for the early diagnosis of different cancers. However, in the presence of high-concentration biomarkers, the signals of lower-expression biomarkers are overlapped. Existing techniques are not suitable for simultaneously detecting multiple biomarkers at concentrations with significantly different orders of magnitude. Here, we propose an asymmetric signal amplification method for simultaneously detecting multiple biomarkers with significantly different levels. Using the bifunctional probe, a linear amplification mode responds to high-concentration markers, and quadratic amplification mode responds to low-concentration markers. With the combined biobarcode probe and hybridization chain reaction (HCR) amplification method, the detection limits of microRNA (miRNA) and ATP via surface-enhanced Raman scattering (SERS) detection are 0.15 fM and 20 nM, respectively, with a breakthrough of detection concentration difference over 11 orders of magnitude. Furthermore, successful determination of miRNA and ATP in cancer cells supports the practicability of the assay. This methodology promises to open an exciting new avenue for the detection of various types of biomolecules.

The impact of outward foreign direct investment on green innovation: the threshold effect of environmental regulation

Abstract: Outward foreign direct investment (OFDI) in an open economy has gradually become an important source of green innovation (GI). With the rapid development of China’s OFDI, this research studies the impact of OFDI on the country’s GI, employing panel data of 30 provinces from 2006 to 2017. We first use the Super-SBM model to measure GI performance and then test the impact of OFDI on GI with the system GMM model. Evidence finds that the negative impact of OFDI on GI is not significant on the whole, but the results of regional regression show that impact of OFDI on GI exhibits obvious regional differences. We then utilize the dynamic threshold panel model to determine the non-linear relationship between OFDI and GI through the perspective of environmental regulation in order to avoid the bias caused by ignoring the impact of institutional factors and time dynamic change. After dividing environmental regulations into command control environmental regulation and market incentive environmental regulation, the research results show that the double threshold effects of both environmental regulations are significant. Command control environmental regulation does not play a role in promoting the effect of OFDI on GI. When the intensity of market incentive environmental regulation is low, OFDI negatively affects GI. Moreover, only when the market incentive regulation shows high intensity can OFDI significantly promote GI. With the continuous growth of China’s OFDI, it is therefore necessary to determine the appropriate environmental regulation to improve the reverse spillover effect of OFDI enterprises on the country’s GI.

Bismuth nanosheets as a Q -switcher for a mid-infrared erbium-doped SrF 2 laser

Abstract: Bismuth nanosheets (Bi-NSs) were successfully prepared and employed as saturable absorbers to generate a diode-pumped dual-wavelength Er3+:SrF2 laser in the mid-infrared region. Q-switched pulses with a maximum output power of 0.226 W were obtained at an absorbed pump power of 1.97 W. A repetition rate of 56.20 kHz and a minimum pulse duration of 980 ns were achieved. To the best of our knowledge, we present the first application of Bi-NSs in a mid-infrared all-solid-state laser. The results prove that Bi-NSs may be applied as an optical modulator in mid-infrared photonic devices or as a mode-locker and Q-switcher.

Cucumber Fruits Detection in Greenhouses Based on Instance Segmentation

Abstract: The cucumber fruits have the same color with leaves and their shapes are all long and narrow, which is different from other common fruits, such as apples, tomatoes, and strawberries, etc. Therefore, cucumber fruits are more difficult to be detected by machine vision in greenhouses for special color and shape. A pixel-wise instance segmentation method, mask region-based convolutional neural network (Mask RCNN) of an improved version, is proposed to detect cucumber fruits. Resnet-101 is selected as the backbone of Mask RCNN with feature pyramid network (FPN). To improve the detection precision, region proposal network (RPN) in original Mask RCNN is improved. Logical green ( LG ) operator is designed to filter non-green background and limit the range of anchor boxes. Besides, the scales and aspect ratios of anchor boxes are also adjusted to fit the size and shape of fruits. Improved Mask RCNN has a better performance on test images. The test results are compared with that of original Mask RCNN, Faster RCNN, you only look once (YOLO) V2 and YOLO V3. The $F_{1}$ score of improved Mask RCNN in test results reaches 89.47%, which is higher than the other methods. The average elapsed time of improved Mask RCNN is 0.3461 s, which is only lower than the original Mask RCNN. Meanwhile, the mean value and standard deviation of location deviation in improved Mask RCNN are 2.10 pixels and 1.73 pixels respectively, which are lower than the other methods.