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43 results on '"Bian, Zhenxing"'

11. Impact and Application of Block Chain Technology on Urban Traffic Based on Artificial Intelligence

Author

Bian, Zhenxing, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Hung, Jason C., editor, Yen, Neil Y., editor, and Chang, Jia-Wei, editor

Published
2022
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14. Development and Application of Artificial Intelligence Technology to Unmanned Driving Under the Background of Wireless Communication

Author

Bian, Zhenxing, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Abawajy, Jemal, editor, Xu, Zheng, editor, Atiquzzaman, Mohammed, editor, and Zhang, Xiaolu, editor

Published
2021
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15. Analysis of Computer Network Information Security in the Era of Big Data

Author

Bian, Zhenxing, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Atiquzzaman, Mohammed, editor, Yen, Neil, editor, and Xu, Zheng, editor

Published
2020
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19. Effect of Expansion of Urban Region on Soil Animals in Farmland Ecosystem: A Case in Shenbei New District in Shenyang City, Liaoning Province

Author

Bian, ZhenXing, Yu, Miao, Li, JinHong, Yu, ZhenRong, Kang, Meng, Junqueira Barbosa, Simone Diniz, Series editor, Chen, Phoebe, Series editor, Cuzzocrea, Alfredo, Series editor, Du, Xiaoyong, Series editor, Filipe, Joaquim, Series editor, Kara, Orhun, Series editor, Kotenko, Igor, Series editor, Sivalingam, Krishna M., Series editor, Ślęzak, Dominik, Series editor, Washio, Takashi, Series editor, Yang, Xiaokang, Series editor, Bian, Fuling, editor, and Xie, Yichun, editor

Published
2015
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26. A Quantitative Survey of Effect of Semi-Natural Habitat Composition and Configuration on Landscape Heterogeneity in Arable Land System

Author

Guo, Xiaoyu, Guan, Minghao, Bian, Zhenxing, Wang, Qiubing, Guo, Xiaoyu, Guan, Minghao, Bian, Zhenxing, and Wang, Qiubing

Abstract

Arable land systems are complex ecosystems composed of cultivated land and semi-nat-ural habitats. Retaining an appropriate proportion of semi-natural habitats in arable land systems is beneficial for enhancing landscape heterogeneity and biodiversity. However, it is unclear how many semi-natural habitats need to be retained in arable land systems to improve landscape heter-ogeneity. In this study, the land use data of four counties were used as the data source in the Lower Liaohe Plain, Liaoning Province, and Rao’s quadratic entropy index (Q) was used to quantitatively characterize the landscape heterogeneity. We aimed to explore the minimum proportion of semi-natural habitat required to maintain high landscape heterogeneity and determine the independent and interactive effects of semi-natural habitat composition and configuration on landscape hetero-geneity. We found that (1) maintaining a 5% proportion of semi-natural habitats is the minimum threshold for achieving high landscape heterogeneity in arable land systems. Retaining a 10% share of semi-natural habitats is beneficial for both agricultural production and land ecology. (2) The com-bination of woodland, water and ditches was good for improving landscape heterogeneity. Connectivity in semi-natural habitats is critical to improving landscape heterogeneity. (3) The interac-tion of semi-natural habitat composition and configuration had a strong effect on landscape heterogeneity (53.1%). Semi-natural habitat configuration was found to be more important than composition for landscape heterogeneity. The role of semi-natural habitat composition and configuration in maintaining landscape heterogeneity and supporting the sustainability of land use therefore needs to be considered in arable land systems.

Published
2022

27. The effect of semi-natural habitat types on epigaeic arthropods : Isolate habitats make critical contribution to biodiversity in agricultural landscape

Author

Guo, Xiaoyu, Bian, Zhenxing, Zhou, Jun, Wang, Shuai, Zhou, Wei, Guo, Xiaoyu, Bian, Zhenxing, Zhou, Jun, Wang, Shuai, and Zhou, Wei

Abstract

Semi-natural habitats in agricultural landscapes vary greatly in shape and vegetation characteristics. They can appear either as relatively large patches of several hectares or as narrow bands (less than 1 m wide) between fields and roads and between fields. Semi-natural habitats support biodiversity and associated ecosystem services on farmland, thereby contributing to sustainable agriculture. However, the effects of different semi-natural habitat types on the polytrophic level of epigaeic arthropods are still unclear. In this study, we classified semi-natural habitats into five types. (1) woody areal (WA), (2) woody line (WL), (3) herbaceous areal (HA), (4) herbaceous line (HL) and (5) isolate habitat (IH). We aimed to explore differences in epigaeic arthropod community composition in five different semi-natural habitats and the effects of different semi-natural habitats and different sampling locations on epigaeic arthropod activity-density and diversity. The results showed that epigaeic arthropod communities show significant differences, not only between farmland and semi-natural habitats, but also between different types of semi-natural habitats. Isolate habitats have the highest activity-density of epigaeic arthropods and Linear herbaceous maintain the highest epigaeic arthropod diversity. We also found in this study, both the activity-density and diversity of epigaeic arthropods showed edge-biased distribution. It may be possible to promote sustainable agricultural ecosystems by understanding the effect of semi-natural habitats on epigaeic arthropods, which is conducive to improving the quality of the agricultural ecological environment.

Published
2022

30. A Review on Carbon Source and Sink in Arable Land Ecosystems

Author

Liu, Xiaochen, primary, Wang, Shuai, additional, Zhuang, Qianlai, additional, Jin, Xinxin, additional, Bian, Zhenxing, additional, Zhou, Mingyi, additional, Meng, Zhuo, additional, Han, Chunlan, additional, Guo, Xiaoyu, additional, Jin, Wenjuan, additional, and Zhang, Yufei, additional

Published
2022
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34. List of contributors

Author

AbdelRahman, Mohamed A.E., Abinash, S., Adhikari, K., Adhyapok, Mrinaljyoti, Anil Kumar, K.S., Anirudhan, S., Archana, K.V., Ashikraj, R.S., Bhagyashree, S., Bhoomika, S.A., Bian, Zhenxing, Bora, Amritee, Brundha, S., Buttar, Harwinder Singh, Chakraborty, Debashis, Chakraborty, Ranabir, Chandramohan, K., Chandrasekar, N., Chittapur, B.M., Ciba, M., Dahiya, Pankaj, Dandu, Mallikarjuna, Das, Bappa, Das, Parinita, Das, Saurav, Dash, Prava Kiran, David Raj, Anu, Desai, B.K., Devendra Prasad, A.G., Dhaloiya, Arvind, Dharumarajan, S., Dutta, Mrinal Kumar, Dutta, Pujarini, Elayapillai, P., Englengpi, Merina, Gan, Chongwei, Ghimire, Deepak, Ghosh, Dibakar, Haldar, Dipanwita, Han, Chunlan, Harikaran, G.K., Hazarika, Prarthana P., Hegde, Rajendra, Hittanagi, P., Jena, Roomesh Kumar, Jhorar, R.K., Jin, Xinxin, Kalaiselvi, B., Kalambukattu, Justin George, Kaleel, Ibrahim, Kaliraj, S., Karthika, K.S., Karunya Lakshmi, K., Khichar, M.L., Khysar Pasha, M., Kiruthika, N., Kondareddy, A.N., Krishnan, K. Anoop, Kumar, Ashish, Kumar, N., Kumar, Sachin, Kumar, Sanjay, Kumar, Satender, Kumar, Suresh, Kumari, Sweta, Kusuma, C.G., Lakshumanan, C., Lal, Narayan, Lalitha, M., Maddileti, N., Mahanta, B., Mallikarjuna, Medhi, B.K., Mehla, Mukesh Kumar, Mipun, B.S., Mohamed, Hisham, Moharana, Pravash Chandra, Mondal, Bhabani Prasad, Naidu, L.G.K., Naitam, Ravindra, Natarajan, A., Niranjana, K.V., Nizar, Asna, P., Sheik Mujabar, Palanivel, K., Panday, Dinesh, Parmar, Manish, Parvathy, S., Patra, Ashok Kumar, Pradeep, C.M., Pradhan, Upendra Kumar, Prakash, B.H., Qian, Fengkui, Raj, G Bhupal, Rajath, Ellur, Rajesh, N.L., Ramamurthy, V., Rameshan, M., Rashmi, I., Ray, Prasenjit, Reddy, Mallikarjun, Rejith, R.G., Roy, Partha Deb, Rudra Bhowmick, Udayan, Saha, Jayant Kumar, Saha, Kaushik, Sahoo, Rabi Narayan, Sahu, Asha, Sahu, Nisha, Satish Kumar, U., Seema, K.V., Senthilvalavan, P., Sharma, Gulshan Kumar, Shashikumar, B.N., Singh, S.K., Sinha, N.K., Sreenivasulu, G., Srinivas, Reji, Srinivas, S., Srinivasan, R., Suputhra, Amar, Thakuria, R.K., Thomas, Pribin Mathew, Upendra, B., Ushakiran, G., Vairaveni, E., Vasundhara, R., Venkatesh, D.H., Vidya, K.N., Vidyashree, Vijayalakshmi, G., Wang, Shuai, Wang, Yan, Wang, Zicheng, Yadav, Brijesh, and Zhang, Xingyu

Published
2024
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39. Applying statistical methods to map soil organic carbon of agricultural lands in northeastern coastal areas of China.

Author

Bian, Zhenxing, Guo, Xiaoyu, Wang, Shuai, Zhuang, Qianlai, Jin, Xinxin, Wang, Qiubing, and Jia, Shuhai

Subjects
*HISTOSOLS, *FARMS, *SOIL mapping, *CARBON in soils, *REGRESSION trees
Abstract

Soil organic carbon (SOC) is an important indicator to evaluate agricultural soil quality. Precise mapping SOC can help to facilitate soil and environmental management decisions. This study applied multiple stepwise regression (MSR), boosted regression trees (BRT) model, and boosted regression trees hybrid residuals kriging (BRTRK) to map SOC of agricultural lands in Wafangdian City, northeastern China. A 10-fold cross-validation procedure was used to evaluate the performance of the three models. The BRTRK method exhibited the best predictive performance and explained 78% of the total SOC variability. The distribution of SOC was mainly explained by elevation, followed by soil-adjusted vegetation index (SAVI), and topographic wetness index (TWI). We conclude that the BRTRK was the most accurate method in predicting spatial distribution of SOC. In addition, our study indicated that topographic variables as key factors to affect SOC should be considered in future SOC mapping. [ABSTRACT FROM AUTHOR]

Published
2020
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