In order to address preserved protein bioactivities and protein sustained-release problems, a method for preparing double-walled microspheres with a core (protein-loaded nanoparticles with a polymer-suspended granule system-formed core) and a second shell (a polymerformed shell) for controlled drug release and preserved protein bioactivities has been developed using (solid-in-oil phase-in-hydrophilic oil-in-water (S/O/O h /W)) phases.The method, based on our previous microsphere preparation method (solid-in-oil phase-in-hydrophilic oil-in-water (S/O/O h /W), employs different concentric poly(D,L-lactide-co-glycolide), poly(D,L-lactide), and protein-loaded nanoparticles to produce a suspended liquid which then self-assembles to form shell-core microspheres in the hydrophilic oil phase, which are then solidified in the water phase.Variations in the preparation parameters allowed complete encapsulation by the shell phase, including the efficient formation of a poly(D,L-lactide) shell encapsulating a protein-loaded nanoparticle-based poly(D,L-lactide-co-glycolide) core.This method produces core-shell doublewalled microspheres that show controlled protein release and preserved protein bioactivities for 60 days.Based upon these results, we concluded that the core-shell double-walled microspheres might be applied for tissue engineering and therapy for chronic diseases, etc.
In order to find out woody wetland plants that can grow in high tidal plats of Chongxi wetlands,by continuous field-monitoring,survival ratio and growth pattern of introduced woody engineering plants were researched.The results indicated that(1)According to the survival ratio of introduced plants,woody plants best adapting to the natural tide included Taxodium distichum,Quercus palustris and Camptotheca acuminate.Better adaptive plants were Adina rubella,Alnus trabeculosa,Casuarina equisetifolia,Pterocarya stenoptera,Bischofia polycarpa,Sapium sebiferum and Euonymus maackii.Species generally adapting to the natural tide included Salix integra,Hibiscus hamabo and Broussonetia papyrifera.Fraxinus griffithii worse adapted to the natural tide.Seven species including Acer negundo,Celtis sinensis,Zelkova serrata,Melia azedaeach,Lycium chinense,Morus alba and Ziziphus jujube,could not adapted to the natural tide.For C.acuminate and B.papyrifera,there was no change of survival ratio between the first and second year,but for other species,their survival ratio all decreased with different degrees respectively.(2)After being introduced for two years,with integrated analyses of height,crown width and breast height diameter indexes,for introduced arbor plants,A.trabeculosa grew best and the most quickly.Other three arbor plants better growing were T.distichum,Q.palustris and C.equisetifolia.For survival introduced shrubs,H.hamabo,S.integra and A.rubella grew better.(3)With integrated analyses with survival ratio and growth pattern indexes,when forested wetlands are constructed at a large scale in Chongxi wetlands in the future,for arbors,A.trabeculosa is most worthy of being introduced.Other three arbor plants worthy of being introduced were T.distichum,Q.palustris and C.equisetifolia.For shrubs,A.rubella was optimum to be introduced.For S.integra and H.hamabo,both may be introduced only with a suitable quantity.With elaborate maintenance and management,they both can take effects of increasing landscape diversity.
Polycyclic aromatic hydrocarbons (PAHs) may accumulate in edible oil from polluted environments or food, which are carcinogenic and cause serious harm to human health. In this study, thin-layer chromatography (TLC) and surface-enhanced Raman scattering (SERS) was used to detect the inset di-naphthalene Raman spectra in different concentrations from edible oil, and the Raman spectral data in the samples were recorded by Raman spectrometaer. Traditional machine learning algorithms of point to point the need for complex smoothing, baseline deduction of data pretreatment such as work, into will cause the data change after the manual intervention, and influence the results of the analysis accuracy, and machine learning can't deeply extract spectral characteristics, this study will Raman spectroscopy combined with deep learning, One-dimensional convolutional neural network (1D-CNN) model was used to predict the concentration of innervated di-naphthalene. Based on the test data of the four characteristic peaks of the original spectrum, spectral features were extracted by convolution at different scales, and the relationship between spectral features and concentration was learned by multi-layer network structure, to establish a more accurate model. Compared with logistic regression, support vector machine, decision tree, and other algorithms, it is verified that the one-dimensional convolutional neural network model has high concentration prediction accuracy and good generalization, with RMSEC only 0.43.
Ground deformation in the coal mining area of Northwestern China is usually featured by large and inconsistent subsidence. Large subsidence brings great challenge to Differential Interferometric Synthetic Aperture Radar (DInSAR) and Permanent Scatterer Interferometry (PSI) technology due to the pixel-level offset in the range direction after co-registration, which could directly result in complete coherence loss even for the artificial Corner Reflectors (CRs). Offset tracking provides an alternative to capture the large deformation although the results could be biased due to the large deformation gradient and use of fixed correlation window size. In this paper, the point-like target (PT) offset tracking method is introduced to deformation monitoring in coal mining area with a levelly decreasing correlation window size. For each PT-centered SLC patch in the reference SAR image, the estimate from the larger correlation window will serve as a guide for the smaller one to locate the matching SLC patch in the slave SAR image, thus providing a guarantee that the residual offsets to be estimated remain to be a small fraction of the decreasing patch size. The offset estimates were evaluated by ground surveying results at 34 CRs obtained using single reference real-time kinematic (RTK) GPS technique, with a RMS error of 1/13-1/14 SLC pixel in both the range and azimuth directions.
Abstract Air pollution has serious harm to the ecological environment and human health. However, current ground monitoring methods and mobile robot traceability methods are difficult to accurately and quickly trace air pollution sources after pollution events. To solve this problem, this paper proposes an air pollution traceability algorithm based on unmanned aerial vehicle (UAV), which combines the mobile and flexible UAV with the hill climb traceability algorithm to realize the monitoring and tracking of air pollution source in a large area. Gaussian concentration field and turbulent concentration field are built by MATLAB, and the simulation experiment is carried out in these two concentration fields. Experimental results show that the algorithm can trace air pollution sources quickly and accurately.
At present, compared to 3D convolution, 2D convolution is less computationally expensive and faster in stereo matching methods based on convolution. However, compared to the initial cost volume generated by calculation using a 3D convolution method, the initial cost volume generated by 2D convolution in the relevant layer lacks rich information, resulting in the area affected by illumination in the disparity map having a lower robustness and thus affecting its accuracy. Therefore, to address the lack of rich cost volume information in the 2D convolution method, this paper proposes a multi-scale adaptive cost attention and adaptive fusion stereo matching network (MCAFNet) based on AANet+. Firstly, the extracted features are used for initial cost calculation, and the cost volume is input into the multi-scale adaptive cost attention module to generate attention weight, which is then combined with the initial cost volume to suppress irrelevant information and enrich the cost volume. Secondly, the cost aggregation part of the model is improved. A multi-scale adaptive fusion module is added to improve the fusion efficiency of cross-scale cost aggregation. In the Scene Flow dataset, the EPE is reduced to 0.66. The error matching rates in the KITTI2012 and KITTI2015 datasets are 1.60% and 2.22%, respectively.
The general situation of botany experiment course with short-term academic year was introduced.The problems facing the setting up of the course were point out.The corresponding solution countermeasures and suggestions were put forward.
Differential interferometric synthetic aperture radar has been shown to be effective for monitoring subsidence in coal mining areas. Phase unwrapping can have a dramatic influence on the monitoring result. In this paper, a filtering-based phase unwrapping algorithm in combination with path-following is introduced to unwrap differential interferograms with high noise in mining areas. It can perform simultaneous noise filtering and phase unwrapping so that the pre-filtering steps can be omitted, thus usually retaining more details and improving the detectable deformation. For the method, the nonlinear measurement model of phase unwrapping is processed using a simplified Cubature Kalman filtering, which is an effective and efficient tool used in many nonlinear fields. Three case studies are designed to evaluate the performance of the method. In Case 1, two tests are designed to evaluate the performance of the method under different factors including the number of multi-looks and path-guiding indexes. The result demonstrates that the unwrapped results are sensitive to the number of multi-looks and that the Fisher Distance is the most suitable path-guiding index for our study. Two case studies are then designed to evaluate the feasibility of the proposed phase unwrapping method based on Cubature Kalman filtering. The results indicate that, compared with the popular Minimum Cost Flow method, the Cubature Kalman filtering-based phase unwrapping can achieve promising results without pre-filtering and is an appropriate method for coal mining areas with high noise.
Winograd algorithm can effectively reduce the computational complexity of convolution operation. Effectively using the parallelism of Winograd convolution algorithm can effectively improve the performance of accelerator architectures on FPGA. The stride represents the number of elements that the window slides when filter is scanned on the input feature map. The Winograd algorithm with the stride of 2 implemented in previous studies divided the input feature maps into multiple groups of Winograd algorithms to complete the operations, resulting in additional precomputation and hardware resource overhead. In this paper, we propose a new Winograd convolution algorithm with the stride of 2. This method uses the unified Winograd transformation matrices instead of the grouping method to complete the calculation. Therefore, the method proposed in this paper can realize 2D Winograd convolution and 3D Winograd convolution by nested 1D Winograd convolution, just like the Winograd convolution algorithm with the stride of 1. In this paper, Winograd transformation matrices with kernel size of 3, 5, and 7 are provided. In particular, for convolution with the kernel of 3, this method reduces the addition operations of Winograd algorithm by 30.0%-31.5% and removes unnecessary shift operations completely. In addition, we implement Winograd convolution algorithm with the stride of 2 through template design, and realize pipeline and data reuse. Compared to the state-of-the-art implementation, the proposed method results in a speedup of 1.24 and reduces resource usage.
This article summarizes and discusses the combination of Producing-Learning-Researching Cooperation of measurement and control technology professional based on the platform of the research and development of automatic dismantling line.In the process of project research and development, many teachers and students and cooperative enterprises participate it, which promote the teachers' scientific research ability, improve the students' practical ability and employment ability and deepen the depth of cooperation between colleges.
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