Mechanisms of the interaction between Pr(DNR)3 and Herring-Sperm DNA

Research on the interaction mechanism of drugs with DNA is essential to understand their pharmacokinetics. The interaction between rare earth complexes Pr(DNR)3 and Herring-Sperm DNA was studied in Tris-HCl buffer solution (pH 7.4) by absorption and fluorescence spectroscopy and viscosity measurements. The results showed that the modes of interaction between Pr(DNR)3 and Herring-Sperm DNA were electrostatic and intercalation. The binding ratio was nPr(DNA)3 ׃ nDNA = 5׃1 and the binding constant was KΘ292K = 4.34×10exp3 L mol-1. Furthermore, according to the double reciprocal method and the thermodynamic equation, the intercalative interaction was cooperatively driven by an enthalpy effect and an entropy effect

A New Study on the Parameter Relationships of Planetary Roller Screws

As a more powerful transmission device, planetary roller screws (PRSs) recently have received more attention, compared to conventional ball screws. However, due to the complicated and unclear relationships among the PRS components’ parameters, it is difficult to design high-quality PRSs. To facilitate the PRS design, a new study on the parameter relationships of PRS is conducted in this work. New models of the axial stiffness and the frictional moment of PRS are developed, and the relationships of the axial stiffness and the frictional moment in terms of contact angle, helical angle, and tooth number of the roller thread are investigated. This study could contribute to the research of PRS to improve its transmission performance, especially to increase its positioning accuracy

A refined non-driving activity classification using a two-stream convolutional neural network

It is of great importance to monitor the driver’s status to achieve an intelligent and safe take-over transition in the level 3 automated driving vehicle. We present a camera-based system to recognise the non-driving activities (NDAs) which may lead to different cognitive capabilities for take-over based on a fusion of spatial and temporal information. The region of interest (ROI) is automatically selected based on the extracted masks of the driver and the object/device interacting with. Then, the RGB image of the ROI (the spatial stream) and its associated current and historical optical flow frames (the temporal stream) are fed into a two-stream convolutional neural network (CNN) for the classification of NDAs. Such an approach is able to identify not only the object/device but also the interaction mode between the object and the driver, which enables a refined NDA classification. In this paper, we evaluated the performance of classifying 10 NDAs with two types of devices (tablet and phone) and 5 types of tasks (emailing, reading, watching videos, web-browsing and gaming) for 10 participants. Results show that the proposed system improves the averaged classification accuracy from 61.0% when using a single spatial stream to 90.5

Demographics, behaviours, and preferences of birdwatchers and their implications for avitourism and avian conservation: A case study of birding in Nonggang, Southern China

Birding, a sustainable ecotourism, capitalizes on the community's rich bird resources to attract an increasing number of birdwatchers. However, the influence of the preferences and behaviour of birdwatchers during birding is unclear. Here, we explore the demographics, behaviours, and preferences of birdwatchers using a case study of birding in Nonggang, southern China. The data was collected from a survey of 201 birdwatchers between April 2017 and April 2018. Results demonstrated that respondents were mainly male, middle-aged, middle-to-high income, and higher-educated. When birding, 96.0% of respondents would photograph birds, and 45.3% prefer photography at fixed-points (i.e., bird-pond photography). Respondents' primary photographic subjects were more likely to be birds with narrower distribution ranges, lower encounter rates, or more feather colors. The majority of the respondents had a strong sense of protection, although the level of awareness against injuring birds was average. Our findings suggest that bird-pond photography has become the dominant form of birding. Solving the relationship between bird photographers' preferences and the conservation of unique species requires an understanding of the rare species and the value of wildlife viewing recreation by humans

Kernel-based feature aggregation framework in point cloud networks

Various effective deep networks have been developed for analysis of 3D point clouds. One key step in these networks is to aggregate the features of orderless points into a compact representation for the cloud. As a typical order-invariant aggregation method, max-pooling has been widely applied. However, while enjoying simplicity and high efficiency, max-pooling does not fully exploit the feature information since it not only ignores the non-maximum elements in each feature dimension but also neglects the interactions between different dimensions. These drawbacks of max-pooling motivate us to explore advanced feature aggregation methods for 3D point cloud analysis. The desired advanced method should be capable of modeling richer information between the point features than max-pooling, and, at the same time, it can readily replace max-pooling without the need to modify other parts of the existing network architecture. To this end, this paper proposes a novel kernel-based feature aggregation framework for 3D point cloud analysis for the first time. The proposed method effectively considers all the elements in each dimension and models the nonlinear interactions between feature dimensions as complementary information to max-pooling. In addition, it is a plug-in module that can be integrated to many common networks as a replacement of max-pooling. Comprehensive experiments are conducted to demonstrate the consistently superior performance and high generality of the proposed method over max-pooling. Specifically, the proposed kernel-based feature aggregation framework consistently improves the max-pooling with three representative backbones of PointNet, DGCNN and PCT across four 3D point cloud based analysis tasks, including supervised 3D object classification, 3D part segmentation, indoor semantic segmentation and one additional unsupervised place retrieval task. Especially, it shows remarkable performance improvement over max-pooling in the unsupervised retrieval task, demonstrating its advantage in forming 3D point cloud representation