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Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. 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 Geography & Geographic Information Science is the property of Geography & Geo-Information Science 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 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.)

Copyright of Tourism Tribune / Lvyou Xuekan is the property of Tourism Institute of Beijing Union University 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 Chongqing University of Technology (Natural Science) 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 Technology & Chinese Language Teaching is the property of Middle Tennessee State University 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 Environmental Science & Technology (10036504) is the property of Editorial Board of Environmental Science & 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 New Architecture is the property of New Architecture 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 Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. 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.)

In order to solve the problem of bypassing detection methods for DNS covert channel（DCC） traffic deformation strategies, this paper proposed a DCC detection method based on traffic slice. In this method, the traffic of the experimental environment was firstly divided into batches based on the sliding window, and then obtained the traffic slice based on the aggregation of each host-end IP. Each slice contains DNS packets and Web packets belonging to the same host in a short time span. Then, for the DNS data volume, DNS request behavior, DNS response behavior, and association behavior correlation behavior with WEB of DNS in the slice, it implement DCC-detection-oriented feature engineering and create detection models on this basis. Comparative experiments show that the detection accuracy of the constructed DCC detection model on the conventional DCC traffic datasets is 99.83%,and the false positive rate is only 0.08%.On the deformed DCC traffic datasets with six types of different traffic deformation strategies, the detection ability of the DCC detection model is more than 95% on average, which is better than the other detection schemes. It proves that the proposed method is effective in dealing with DCC traffic deformation. Moreover, the proposed method, which can effectively detect DCC communication within a single traffic slice, is a detection method with good real-time performance. [ABSTRACT FROM AUTHOR]

Copyright of New Architecture is the property of New Architecture 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 Journal Of Sichuan University (Natural Sciences Division) / Sichuan Daxue Xuebao-Ziran Kexueban is the property of Editorial Department of Journal of Sichuan University Natural Science Edition 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.)

[Objectives] Weaned piglets are the focus of attention in pig breeding industry. Accurate tracking of piglets in group breeding environment is the basis of analyzing individual behavior and monitoring individual health of piglets. This paper proposed a target tracking method for weaned piglets based on deep learning. [Methods] The detection model of weaned piglets was designed by using centernet detection algorithm based on center point to optimize the detection part of DeepSORT algorithm. The model was trained and optimized for extracting piglet target appearance characteristics during tracking. Combined with Kalman filter and Hungarian matching algorithm, the re-identification was improved to realize the real-time detection and tracking of weaned piglets with deep correlation measurement. [Results] The algorithm test results showed that the mean average precision and recall of weaned piglets were 99.0% and 78.6% respectively. The multi-target tracking accuracy indexes of MOTA (multiple object tracking accuracy) and MOTP (multiple object tracking precision) were 96.8% and 81.8%, respectively. [Conclusions] The method proposed in this paper can improve the tracking difficulty caused by the high similarity of weaned piglets' appearance and adhesion occlusion to some extent. It can accurately track the weaned piglets in the herd environment, and provide technical support for the subsequent individual behavior analysis of piglets. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Agro-Environment Science is the property of Journal of Agro-Environment Science Editorial Board 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.)

[Objectives]The posture of meat goose is an important indicator for early warning of the meat goose’s abnormal behavior and evaluation of the meat goose’s health status. Since manual observation of the meat goose’s posture on traditional farms was time-consuming, laborious and highly subjective, a new method based on deep learning was proposed in this paper. The detection algorithm recognized the posture of meat goose automatically. [Methods]The YOLO v5 model was used to identify the four postures of Yangzhou goose (standing, resting, drinking and feather preening). Three attention modules including SENet, CBAM and ECA were added to the YOLO v5 model to improve the network structure and the recognition ability of the model. Light and dark experiments and dense scene experiments were designed to further verify the robustness of the model in complex scenes. [Results]The average detection accuracy (mAP) of the YOLO v5+ECA model was 88.93%,which was 2.27% higher than that of YOLO v5. In terms of recognition accuracy (AP), the standing posture was 91.85%,the resting posture was 93.42%,the drinking posture was 90.02%,and the feather preening posture was 80.42%. In the light and dark experiments and dense scene experiments, the performance of the YOLO v5+ECA model was stable, and the missing detection and false detection were relatively rare. [Conclusions]The posture of the meat goose can be detected rapidly and accurately in the farm complex scene with this model. The data can support the following research on the behavior monitoring and epidemic prevention of the meat goose. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shanghai Physical Education Institute / Shanghai Tiyu Xueyuan Xuebao is the property of Shanghai Physical Education Institute 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 Image & Graphics is the property of Editorial Office of Journal of Image & Graphics 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.)

Based on the analysis of the problems existing in the admission of evaluation experts, the classification of evaluation majors, the use and extraction of evaluation experts, and the inspection and supervision, and in the light of the characteristics of the talent level, quantity scale, subject distribution and behavior of evaluation experts in universities, this paper puts forward some suggestions on optimizing management and quality service, including enhancing the sense of honor of experts, innovating and optimizing the business training mode, constructing compatible multi-criteria professional classification model to support multi-dimensional intelligent growth, and establishing annual qualification inspection system and so on. This paper expounds the necessity and feasibility of establishing "national expert sharing network of colleges and universities" to promote the work of sharing university experts across the country by unifying and standardizing data standards, establishing inter-university expert resource sharing mechanism, building intelligent extraction and efficient notification business system and other guarantee measures, and complying with the path from sharing in the same city to regional sharing in provinces and municipalities, and ultimately national sharing. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunication Engineering is the property of Telecommunication 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 Journal of Agricultural Science & Technology (1008-0864) is the property of Journal of Agricultural Science & 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 Changzhou University (Social Science Edition) is the property of Editorial Department of Journal of Changzhou University 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.)

[Objectives] Real-time perception and behavior analysis of individual cow information are the inevitable requirements of modern dairy cow fine breeding. Effective identification of individual cow identity is the premise and basis of the above goals. The contact-free recognition method based on the image of the cow’s biological characteristics(faces, body spots, etc.) is susceptible to external interference and the algorithm complexity is high, and the identifiable sample size is limited. Therefore, this paper proposes a method of automatic location and recognition of cow’s collar ID based on deep learning. [Methods] Aiming at the ID deflection problem of neck ring caused by cow movement, the cascade detector based on histogram of oriented gradient(HOG) feature combined with multi-angle detection method was adopted to realize the localization of cow signs. A single character image was obtained by a series of processing such as image enhancement and binary segmentation. The structure and parameters of the convolutional neural network were designed to train the character recognition model so as to complete the recognition of signage characters. The experimental data included 1 414 side-looking images of 80 cows, of which 58 were randomly selected as the training set and the images of the remaining 22 cows as the test set. [Results] The accuracy of placards was 96.98%,the recall rate was 80.23%,the accuracy of the character recognition model was 93.35%,and the recognition rate of individual cows in continuous image sequences was 95.45%. [Conclusions] The recognition model has good robustness to light change, stain contamination, rotation angle and so on, which has the potential to replace the traditional animal individual identification method. [ABSTRACT FROM AUTHOR]

Etomidate (ET) is widely used as an anesthetic in clinical practice. The long-term use of ET can lead to negative effects, such as decreased consciousness and cognitive impairment. However, the developmental neurotoxicity of ET on fish remains unclear. To address this issue, zebrafish embryos were exposed to ET from 2 hpf (hours post-fertilization) to 168 hpf at 0. 010, 0. 091, 0. 501, 9. 400, 84. 31 and 664. 4 μg·L-1. The physiological development, behavior and cell apoptosis, and transcriptional expression of genes related to dopamine (DA) and γ-aminobutyric acid (GABA) pathway were detected to investigate the developmental neurotoxicity of ET. The results showed that 664.4 μg·L-1 ET decreased hatching rate at 48 hpf and 60 hpf. 0.091 μg·L-1 and 0.501 μg· L-1 ET decreased body length in larvae at 168 hpf. At 48 hpf, 0.010, 0.091, 0.501, 9.400, 84.31 and 664.4 μg·L-1 ET increased malformation. 0.501, 9.400, 84.31 and 664.4 μg·L-1 ET increased malformation at 96 hpf. Additionally, 0.501, 9.400 and 664.4 μg·L-1 ET increased malformation at 168 hpf. The 0.010 μg·L-1 ET caused cell apoptosis in the brain, while 664.4 μg·L-1 ET inhibited apoptosis. In addition, behavior analysis indicated that ET inhibited touch behavior at all concentrations. The 0.091 μg·L-1 ET enhanced swimming behavior in zebrafish larvae, while 664.4 μg·L-1 ET weakened swimming behavior. ET increased anxious behavior in a concentration-dependent manner. The qPCR data showed that ET up-regulated the transcriptions of genes related to DA and GABA pathway at low concentration, while ET down-regulated the transcriptions of these genes at high concentration. The above results indicate that ET has significant neurodevelopmental toxicity in early development of zebrafish. [ABSTRACT FROM AUTHOR]

n order to quantitatively describe the influencing factors of self - driving tourists' choice of berth sharing behaviors in scenic spots, and analyze the parking behaviors of self - driving tourists, scientific and reasonable berth sharing planning strategies of scenic spots can be formulated. Through Logistic model, this paper establishes a behavioral analysis model of sharing berth selection in tourist scenic spots based on the perspective of parkers. The study shO'WB that: berthing time and walking distance are the most important factors for self - driving tourists to choose to share berths, and their influence degree accounts for 43% and 33% respectively, while income, parking time and parking cost have little influence on their berth selection. In addition, when tourists are more familiar with the traffic conditions around the scenic area and it is more difficult to find a parking space for driving, they will have a stronger willingness to choose a shared berth. Accordingly, measures and suggestions for improving berth sharing planning and improving berth sharing efficiency are put forward [ABSTRACT FROM AUTHOR]

Copyright of Journal of Central China Normal University is the property of Huazhong Normal University 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 optimize the game experience, the game platform often provides decision-making AI hosting functions, which causes the platform to face low data quality when presenting images to users. The log mixes the AI and the decision-making behavior made by the player, and sometimes there is a lack of behavior. This paper proposed a behavior extraction and analysis method for decision-oriented hosting applications. Through the behavior feature extraction method based on heuristic rules, this method extracted the player' s autonomic behavior from the log. Finally, it used a variety of portrait methods to analyze and compare the results on the original data set and the reduced data set respectively. It is concluded that the method can greatly reduce the amount of data while retaining the player's behavior characteristics. [ABSTRACT FROM AUTHOR]

Using the data mining technology to analyze the network learning behavior data can dig out its hidden behavior characteristics, and provide personalized learning resource services for learners . Aiming at the problem that the model applicability of the existing data mining algorithms gene rally was not very high when analyzing of network learning behavior data, this paper proposed a learning resource recommendation algorithm based on behavior sequence analysis. Firstly, this pap er proposed the definition of behavior sequence and its related concepts. Secondly, it proposed the calculation method of behavior sequence similarity. Then, it proposed the collaborative filtering recommendation algorithm based on behavioral sequence similarity to calculate the similarity between learners and to generate the learning resource recommendation list for the learner to be recommended. And then it gave the recommendation algorithm based on learning style, and integrated learners ' learning style characteristics into the recommendation process. Finally, it presented the learning resource recommendation algorithm based on behavior sequence analysis. The proposed algorithm does not limit the pattern of behavior sequence, and has a high applicability, it can be used for further study of the network learning behavior data and provide personalized learning service for learners. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Architecture & Civil Engineering is the property of Chang'an Daxue Zazhishe 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.)

Aimed at the present issues on teachers’ teaching quality evaluation behavior deviation widespread among domestic institutions and universities, based on the present teaching quality evaluation index, from three aspects about the historical role and teaching orientation, policy environment and conditions of the hardware and, the index system and evaluation conclusion, this paper analyzes the reasons for teachers’ experiment and training teaching quality evaluation behavior deviation. And a reform program is proposed. It contains that at present stage colleges and universities should strengthen the recognition of the equal status of experimental training and classroom teaching, increase the support for experimental training and adhere to the combination of quantitative and qualitative way to build a scientific and reasonable experiment and training teaching quality evaluation system reforms. Combined with the reform practice, the guiding ideology, implementation method, evaluation index and specific requirements of the reform plan are discussed in detail. This research could provide a reference function on experiment and practice teaching quality construction practice for science and engineering majors in domestic colleges and universities. [ABSTRACT FROM AUTHOR]

The dense medium cyclone is widely used in coal separation process. The experimental study on internal flow field and particle motion characteristics of dense medium cyclone is difficult and time-consuming with high cost, due to the complicated separation process. With the development of numerical calculation technology, the numerical simulation method was widely used to study the multiphase flow field inside the cyclone. In this paper, the computational fluid dynamics (CFD) and discrete analysis (DEM) coupling techniques were used to numerically simulate the separation process of dense medium cyclones, which provided a new way to optimize the structural parameters and operating parameters of dense medium cyclones. The Fluent software was used to study the velocity field, density field, pressure gradient field and viscosity field of the internal suspension of the cyclone. The EDEM software was used to study the coal particle movement behavior and the evaluation of separation efficiency during the cyclone separation process. The results showed that the pressure distribution and the pressure gradient of suspension were basically symmetrical, and the pressures at the overflow and underflow outlet were the lowest. The pressure gradient was formed along the radial direction, the difference gradually increased, and the largest pressure gradient was at the boundary of the air column. The coal particles in different sizes had different residence time inside the cyclone. For the coal particles of same density, the smaller the particle size, the longer the residence time. The residence time of the discharged coal particles in the overflow in the cyclone was significantly longer than that from the underflow outlet. For coal particles discharged from the overflow outlet, the higher the density, the longer the residence time. While for coal particles discharged from the underflow outlet, the higher the density, the shorter the residence time. The structural parameters of different cyclones had different effects on separation process, and the influence of the diameter of the overflow pipe was the most significant. When the diameter of the overflow pipe exceeded 500 mm, a complete air column cannot be formed, resulting in failure to separation. When the diameter of the overflow pipe was 300 mm, the separation efficiency was better. In addition, the insertion depth of the over-flow pipe significantly affected the separation efficiency. When the insertion depth was 160 mm, the separation density increased, and the fine high-density coal particles would misplace into the overflow. When the insertion depth of the overflow was 320-800 mm, the separation density was close to the suspension density, Ep value was 0. 084-0. 100, and the separation efficiency was better. The diameter of the underflow outlet had a great influence on the separation precision of the cyclone. When the diameter was 272 mm and 306 mm, the separation density was close to the density of the suspension, and the Ep value was less than 0. 1, which had a better separation efficiency. The length of the cylindrical section had little effect on the separation density. [ABSTRACT FROM AUTHOR]