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As vital infrastructures, high-speed railways support the development of transportation. To maintain the punctuality and safety of railway systems, researchers have employed manual and computer vision methods to monitor overhead contact systems (OCSs), but they have low efficiency. Investigators have also used light detection and ranging (LiDAR) to generate point clouds by emitting laser beams. The point cloud is segmented for automatic OCS recognition, which improves recognition efficiency. However, existing LiDAR point cloud segmentation methods have high computational/model complexity and latency. In addition, they cannot adapt to embedded devices with different architectures. To overcome these issues, this article presents a lightweight neural network EffNet consisting of three modules: ExtractA, AttenA, and AttenB. ExtractA extracts the features from the disordered and irregular point clouds of an OCS. AttenA keeps information flowing in EffNet while extracting useful features. AttenB uses channel and spatialwise statistics to enhance important features and suppress unimportant ones efficiently. To further speed up EffNet and match it with diverse architectures, we optimized it with a generation framework of tensor programs and deployed it on embedded systems with different architectures. Extensive experiments demonstrated that EffNet has at least a 0.57% higher mean accuracy, but with 25.00% and 9.30% lower computational and model complexity for OCS recognition than others, respectively. The optimized EffNet can be adapted to different architectures. Its latency decreased by 51.97%, 56.47%, 63.63%, 82.58%, 85.85%, and 91.97% on the NVIDIA Nano CPU, TX2 CPU, UP Board CPU, Nano GPU, TX2 GPU, and RTX 2,080 Ti GPU, respectively.

Table of contents 01 The influence of soil salt content on the photosynthetic characteristics of spring wheat with trickle irrigation Lei Pei, Zhenhua Wang, Jinzhu Zhang, Wenhao Li 02 Comparing growth of fast-growing and slow-growing endophytes in plants via ergosterol quantification Adeline SY Ting, Yiing Y Chow, Sadequr Rahman 03 Transcriptome and digital gene expression analysis identifies putative triterpene saponin-biosynthetic genes of Panax notoginseng Rongchang Wei 04 Chitosan-assisted isolation and antioxidant evaluation of flavonoids from Sophora japonica L. Zhang Hu, Sidong Li, Chuyan Li, Zijuan Li 05 Two kinds of new characteristics of the ektexine ornamentation of ginkgo pollen Guoxia Wang, Yuzhen Yang, Gang Chen, Qing Luo 06 Analysis of nutrient and medicinal ingredients of Ginkgo pollen in different regions Guoxia Wang, Ruixia Liu, Yuzhen Yang, Lipei Chen 07 Photosynthetic performance of greening seedlings of seven species to drought stress Zhiyang Lie, Tongtong Zhou, Weilong Huang, Li Xue 08 Changes of fluorescence parameters of greening seedlings of seven species under drought stress Jie Li, Zhuomin Wang, Li Xue 09 Mammalian sex hormone affects regeneration capacity and enzymes activity of Triticale L in vitro culture Ismail Bezirganoglu, Pınar Uysal 10 Fractional Fourier entropy increases the recognition rate of fruit type detection Shuihua Wang, Zhihai Lu, Jiquan Yang, Yudong Zhang, John Liu, Ling Wei, Shufang Chen, Preetha Phillips, Zhengchao Dong 11 Banana-peanut intercropping reduces Fusarium wilt disease in banana from enhancing soil bacterial microorganisms and leaf nutrition Hong Li, Xutong Wang, Fengliang Zhao, Guisheng Yang 12 Manganese stress impairs stem ureide nitrogen fixation in yardlong-bean plants in the acidic environment Hong Li 13 A new pest control method for Rhytidodera bowringii Larvae Haijie Huang, Li Zhao, Weijian Huang, Jinhui Wang, Zhongrun Zhang 14 Research on the seed-like Fruits of Subg. Sclarea of Salvia of Labiatae in China Xiaojuan Li, Ning Xu, Guofu Zhou, Ming Wan, Qi Lin, Fanyun Meng, Jianxiu Li 15 Three pulling resistance models of pioneer plant in landslide area Yichang Chen, Koayung Yu, Chunpin Chang 16 The comparison of physiological and biochemical mechanisms of Reaumuria soongorica and Salsola passerine in different growth pattern Zijuan Zhou, Peixi Su, Rui Shi, Tingting Xie 17 Resources use efficiency of the cosmopolitan plant Potentilla anserina L. in different alpine habitats in China Rui Shi, Peixi Su, Zijuan Zhou 18 Cloning of PPDK gene from Red Amaranand transformation of Alfalfa Xuelan Liu, Yan Zhang, Xiangfa Wei 19 Variation and cluster analysis of morphological characters and nutrient content of Chucrasia tabularis seed Chong Wu, Yanlei Yin, Lijuan Feng, Xuemei Yang, Fei Wang 20 Effect of the planting density of the areca nut on the growth of intercropped Vanilla Hua Wang, Huifa Zhuang, Zihui Zhu, Hui Wang

As overhead contact (OC) is an essential part of power supply systems in high-speed railways, it is necessary to regularly inspect and repair abnormal OC components. Relative to manual inspection, applying LiDAR (light detection and ranging) to OC inspection can improve efficiency, accuracy, and safety, but it faces challenges to efficiently and effectively segment LiDAR point cloud data and identify catenary components. Recent deep learning-based recognition methods are rarely employed to recognize OC components, because they have high computational complexity, while their accuracy needs to be improved. To track these problems, we first propose a lightweight model, RobotNet, with depthwise and pointwise convolutions and an attention module to recognize the point cloud. Second, we optimize RobotNet to accelerate its recognition speed on embedded devices using an existing compilation tool. Third, we design software to facilitate the visualization of point cloud data. Our software can not only display a large amount of point cloud data, but also visualize the details of OC components. Extensive experiments demonstrate that RobotNet recognizes OC components more accurately and efficiently than others. The inference speed of the optimized RobotNet increases by an order of magnitude. RobotNet has lower computational complexity than other studies. The visualization results also show that our recognition method is effective.

High-speed railways have been one of the most popular means of transportation all over the world. As an important part of the high-speed railway power supply system, the overhead catenary system (OCS) directly influences the stable operation of the railway, so regular inspection and maintenance are essential. Now manual inspection is too inefficient and high-cost to fit the requirements for high-speed railway operation, and automatic inspection becomes a trend. The 3D information in the point cloud is useful for geometric parameter measurement in the catenary inspection. Thus it is significant to recognize the components of OCS from the point cloud data collected by the inspection equipment, which promotes the automation of parameter measurement. In this paper, we present a novel method based on deep learning to recognize point clouds of OCS components. The method identifies the context of each single frame point cloud by a convolutional neural network (CNN) and combines some single frame data based on classification results, then inputs them into a segmentation network to identify OCS components. To verify the method, we build a point cloud dataset of OCS components that contains eight categories. The experimental results demonstrate that the proposed method can detect OCS components with high accuracy. Our work can be applied to the real OCS components detection and has great practical significance for OCS automatic inspection.

The overhead contact system (OCS) is a critical railway infrastructure for train power supply. Periodic inspections, aiming at acquiring the operational condition of the OCS and detecting problems, are necessary to guarantee the safety of railway operations. One of the OCS inspection means is to analyze data of point clouds collected by mobile 2D LiDAR. Recognizing OCS components from the collected point clouds is a critical task of the data analysis. However, the complex composition of OCS makes the task difficult. To solve the problem of recognizing multiple OCS components, we propose a new deep learning-based method to conduct semantic segmentation on the point cloud collected by mobile 2D LiDAR. Both online data processing and batch data processing are supported because our method is designed to classify points into meaningful categories of objects scan line by scan line. Local features are important for the success of point cloud semantic segmentation. Thus, we design an iterative point partitioning algorithm and a module named as Spatial Fusion Network, which are two critical components of our method for multi-scale local feature extraction. We evaluate our method on point clouds where sixteen categories of common OCS components have been manually labeled. Experimental results show that our method is effective in multiple object recognition since mean Intersection-over-Unions (mIoUs) of online data processing and batch data processing are, respectively, 96.12% and 97.17%.

In the rice-wheat rotation system, biochar (BC) can be applied at the initiation of the rice or wheat season. Here, we compared the effects of BC that were applied at two different crop seasons on wheat production, quality, and soil profile properties in a rice-wheat rotation system with nitrogen (N) fertilizer applied at 280 kg/ha rate. Results showed that both wheat grain production and N recovery use efficiency were influenced by BC applied at two crop seasons. Biochar application did not affect the total non-essential amino-acid, but when applied during wheat season, BC significantly (p < 0.05) increased total essential amino acid in grain by 12.3%, particularly for the valine (+48.2%), methionine (+43.8%), and isoleucine (+10.3%). We found that BC significantly (p < 0.05) decreased the pH of soil at 0-6 cm and 20-30 cm by 0.14-0.18 and 0.05-0.08 units, respectively. The NH4+-N content of the whole observed soil profile were reduced by BC application, however, the effect of BC on NO3--N content varied with the application season and profile depth. Interestingly, BC applied at wheat and rice season significantly (p < 0.05) improved topsoil N contents by 48.4% and 19.7%, respectively. In addition, data suggested that BC applied during wheat season performed better in enhancing soil available phosphorus, potassium, and organic matter contents. In conclusion, we suggest that the optimum application time of BC for enhancing crop production and quality (take amino-acid content for example) and improving soil fertility is at the initiation of the wheat season. [ABSTRACT FROM AUTHOR]

In order to have knowledge about the college students' awareness and acceptance of genetically modified foods and their attitudes toward the identification of genetically modified foods and the government's regulation, we conduct a questionnaire survey of 150 college students' cognition of genetically modified foods in North College Town of Zhengzhou City. The results show that the college students have a certain understanding of genetically modified foods, but the cognition level is low; electronic media has become the main channel for the current college students to know the information about genetically modified foods; for security reasons, the majority of college students are wary of genetically modified foods, and pay more attention to whether there is genetically modified component in the foods labeling; college students generally believe that the government should strengthen the supervision of genetically modified foods, and make the GM labeling system strict, to protect consumers' right to know and choice. [ABSTRACT FROM AUTHOR]

Ginkgo pollens are rich in essential amino acids, vitamins, flavonoids and other ingredients, can be widely used in the development of health products and other nutrition utilization, but whether there are differences between Ginkgo pollens from different regions had not been finalized. The contents of vitamin B1, vitamin B2, vitamin C, amino acids, total flavonoids of Ginkgo pollen were measured from 18 Chinese Cities. The average contents of vitamin B1, vitamin B2, vitamin C, amino acids, total flavonoids of Ginkgo pollens of 18 cities were respectively 0.1211 mg.g-1, 0.6476 mg.g-1, 0.1135 mg.g-1, 225.2 mg.g-1 and 27.41 mg.g-1. The highest three of vitamin B1 were Gunlin, Lueyang and Pizhou amount the 18 regions, respectively was 0.2239 mg.g-1, 0.2167 mg.g-1 and 0.1912 mg.g-1; the highest three of vitamin B2 were Pizhou, Fenghua and Doujiangyan, respectively was 1.1875 mg.g-1, 1.0442 mg.g-1and 0.9333 mg.g-1; the highest three of vitamin C were Xixia, Fenghua and Tengchong, respectively was 0.2061 mg.g-1, 0.1882 mg.g-1 and 0.1828 mg.g-1; the highest three of amino acids were Guilin, Changxing and Xinxian, respectively was 253.3 mg.g-1, 252.8 mg.g-1 and 249.8 mg.g-1; the highest three of total flavonoids were Nanxiong, Kangxian and Lueyang, respectively was 36.58 mg.g-1, 32.93 mg.g-1 and 31.80 mg.g-1. The result showed that there were significant differences in the nutrient and medicinal ingredients from diffrent regions. [ABSTRACT FROM AUTHOR]

Objective A study was conducted to compare the effect of different enamel remineralization periods after erosion on the depth of brushing abrasion. Methods Ten volunteers were selected for a 4-day experiment. A total of 60 enamels were randomly assigned into six groups (A-F) and placed in intraoral palatal devices. On the first day, the palatal devices were placed in oral cavity (24 h) . On the following three days, brushing experiments were performed extraorally, two times per day. The specific experimental method of brushing follows these next steps. First, the group F specimens were covered with a film of wax, and then acid etched for 2 min. Subsequently, the film of wax was detached. The groups from A to D were brushed after remineralization at the following time intervals: group A, 0 min; group B, 20 min; group C, 40 min; group D, 60 min. Erosion and remineralization were performed on group E, but without brushing. Remineralization was performed on group F, but without acid etching and brushing. The depth of enamel abrasion was determined by a mechanical profilometer. The surface morphology of the enamel blocks was observed using a scanning electron microscope. Results 1) The depth of abrasion was different in varied enamel remineralization time after acid etching. The statistical significant differences between groups were as follows. 2) When the time of enamel remineralization after acid etching was short, the surface depression in the electron microscope was deep, and the surface morphology was rough. Conclusion Brushing immediately after acid etching would cause much serious abrasion to the enamel surface. Brushing after 60 min can effectively reduce the abrasion of acid etching enamel.