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In order to determine the most suitable intensity of thinning for soil microbial enzyme activity of natural broad-leaved mixed forest in Xiaoxing'anling region, 6 plots of thinning plots (10%, 15%, 20%, 25%, 30%, 35%) were set up in Dongfanghong Forest Farm of Dailing Forestry Experimental Bureau and the corresponding plots without thinning were set up, recorded as plot A, B, C, D, E, F and CK, respectively. Based on the field survey in 2021, soil samples of 0-20 cm soil layer were taken from each treatment plot. The effects of thinning intensity on soil microbial enzyme activities were analyzed by single factor analysis of variance, minimum significant difference method and Pearson method. The results showed that the mass fractions of soil organic carbon, microbial biomass carbon, total nitrogen and microbial biomass nitrogen in tending and thinning plots were higher than those in control plots, and the differences were significant (P<0. 05). Moderate thinning increased soil microbial respiration. The activities of soil sucrase, soil catalase and soil acid phosphatase increased compared to the control plots, and the difference was significant, while the activities of soil urease decreased compared to the control plots. The activities of soil sucrase, soil catalase and soil acid phosphatase were significantly positively correlated with the mass fraction of organic carbon, total nitrogen, microbial biomass carbon and microbial biomass nitrogen (P<0. 05), while the activities of soil urease were not correlated with other indexes (P>0. 05). Proper thinning can promote the accumulation of soil nutrients, including increasing soil organic carbon, total nitrogen, microbial biomass carbon and microbial biomass nitrogen mass fraction, increasing soil microbial respiration rate, and improving the activities of soil sucrase, catalase and acid phosphatase. However, compared to control plots, tending thinning results in the decrease of soil urease activity. The results showed that 20%-30% thinning intensity was the best for improving soil microbial enzyme activity in the natural mixed forest in Xiaoxing'an Mountains. [ABSTRACT FROM AUTHOR]

Copyright of Asian Journals of Ecotoxicology is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Asian Journals of Ecotoxicology is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to solve the problem of continuous leakage dispersion of gaseous pollutants, the Gaussian puff model was improved by using the method of time segmentation processing and coordinate transformation to enable it to calculate the continuous leakage and wind direction change of point sources. A simulation program based on Python was developed to calculate and analyze the effects of wind speed and atmospheric stability on pollutant diffusion. The results show that the model implements the calculation of the concentration distribution of pollutant diffusion under changing wind conditions and continuous leakage scenarios. The area of each level dangerous area increases with the increase of diffusion time, and then tends to stabilize. The time required to reach stability and the area of the hazardous area after stabilization decreases with the increases of wind speed, and decreases with the deterioration of atmospheric stability. In the case of each wind speed and each atmospheric stability, the area of the moderate dangerous area is higher than other areas, should be focused on protection. [ABSTRACT FROM AUTHOR]

Copyright of World Nuclear Geoscience is the property of World Nuclear Geoscience Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of World Nuclear Geoscience is the property of World Nuclear Geoscience Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

: In order to monitor illegal land use in real time, video surveillance technology was used to monitor the vulnerable areas of illegal land use. Excavator was one of the most important construction machinery in the engineering construction, an automatic real-time detection of excavator could provide important information for non-contact field monitoring of illegal land. But it was difficult to accurately detect the excavator due to the complex background, uneven illumination and partial occlusion in natural scene, This paper proposed a real-time excavator detection algorithm in natural scene based on the SSD-like (Single Shot Detector). Specifically, the lightweight network DDB (Depthwise Dense Block) was used as the basic network to extract shallow feature and fuse with high-level features in the excavator objection model to enhance the feature representation capability. Meanwhile, the BDM (Bottleneck Down-sampling Module) which was designed based on the lightweight network MobileNetV2 was used as the multi-scale feature extraction network to reduce the parameter quantity and computation. The data sets included 18 537 images of excavators with different shooting angles and natural scenes, 15 009 images were used as training set and 3 528 images were chosen as test set randomly. To enhance the diversity of training data, data set expansion methods such as rotation and image were adopted. Based on the Caffe deep learning framework, the proposed model in this paper was trained with end-to-end approximate joint methods and the model weight was fine-tuned by using SGD (Stochastic Gradient Descent) algorithm. The DDB module of the network was initialized with the weights pre-trained on the PASCAL VOC dataset, and the training time and resources were further reduced by transferring learning. Then the model pre-trained on the large data sets was transplanted to excavator object detection by transfer learning. The proposed method was transplanted and performed on the mainstream Jetson TX1 embedded hardware platform, and experiments on the actual data set of detecting excavator object at different angles of view and natural scenes. Experiment results showed that the parameter quantity and computational complexity of proposed model with BDM was reduced by 68.4% compared to SSD, the mAP (Mean Average Precision) of proposed method reached 90.6% on the testing set, which was 0.4% and 0.4% higher than that of SSD based on VGG16 basic net and MobileNetV2SSD based on MobileNetV2 basic net, respectively. The model size of propose method was 4.2 MB, which was about 1/25 and 1/4 of SSD and mobilenetv2ssd, respectively, and the time-consuming of each frame was 145.2 ms, which was 122.7% and 28.2% faster than SSD and MobileNetV2SSD, respectively. The proposed method not only had better generalization and robustness, but also can be better deployed on the embedded hardware platform which demonstrated the feasibility of real-time monitoring of the excavator at site of illegal land use. [ABSTRACT FROM AUTHOR]

Copyright of Nuclear Safety is the property of Nuclear & Radiation Safety Center and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To achieve the effective real-time monitoring for illegal use of land, it is very important to recognize the working state of the excavators and other construction machinery on the ground. Under the natural scene, the strong influences of random noise interference and uneven illumination result in a great difficulty inidentifying the working state of excavators. This paper proposes a new method for the recognition of excavator working state based on video image. First, the mixed local binary features (LBF) shape regression model of excavators with various postures is trained offline, and this model is used to predict the shape (the relative coordinate sets of the features) of the input video frames. Then, by using the coordinates of the features and the length-width ratio of the excavator, the feature descriptor of the excavator working state is designed. Finally, a support vector machine (SVM) classifier is used to identify the current state (working or not working) of the excavator. The experimental results reveal that our algorithm can overcome the influence of the shape changes caused by multi-pose excavators and achieves an accuracy of 93.53% in the excavator state recognition. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Northeastern University (Social Science) / Dongbei Daxue Xuebao (Shehui Kexue Ban) is the property of Dongbei Daxue and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Northeastern University (Natural Science) is the property of Dongbei Daxue Xuebao and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Foreign Literature Studies is the property of Foreign Literature Studies and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of Beijing Institute of Technology is the property of Beijing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective: To study the relationship between carotid atherosclerosis, aortic valve calcification(AVC) and atherosclerotic renal artery stenosis(ARAS). Methods: 162 cases of patients with chest pain, who underwent renoarteriography, were selected and divided into ARAS group(29) and control group(133) according to the angiographic results. The age, gender, history of smoking, hypertension, diabetes, hyperlipidemia and coronary heard disease were recorded and compared between two groups. The total cholesterol(TC), triglyceride(TG), Low-density lipoprotein(LDL-C), high-density lipoprotein(HDL-C) levels of patients were measured,and the carotid artery intima-media thickness(CIMT), carotid atherosclerosis and aortic valve calcification(AVC) were investigated by carotid ultrasonography and echocardiogram. Results: The constituent ratio of hypertension history, TC, CIMT, incidence of carotid atherosclerosis and AVC were significantly higher in the patients of ARAS group than those of the control group. The sensitiviy,speciality of carotid atherosclerosis for predicting the ARAS were 72.24%, 56.39%, and the sensitiviy, speciality of AVC were 44.83%,74.45%. The sensitiviy, speciality of combination of carotid atherosclerosis and AVC were 41.38%, 76.69%. Conclusion: Carotid atherosclerosis and AVC were useful for the diagnosis of ARAS, which could be used to exclude the ARAS. [ABSTRACT FROM AUTHOR]

Copyright of Electric Machines & Control / Dianji Yu Kongzhi Xuebao is the property of Electric Machines & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)