"Internet + Special Agriculture" Drives Rural Green Revitalization - Investigation and Analysis of Xunwu County, Jiangxi Province

"Internet + agriculture” organically combines Internet technology with traditional agriculture to achieve agricultural transformation and upgrading. Under the strategy of rural rejuvenation, Xunwu County, Jiangxi Province, developed "Internet + characteristic agriculture" according to local conditions, which has optimized the local agricultural scale and characteristic agricultural formats. However, the "Internet + characteristic agriculture" in Jiangxi Province still has problems such as imbalanced structural development, low level of inclusive finance, and insufficient professional talent reserves. It is necessary to promote the industrial and financial development of various agricultural sectors and the Internet, increase policy support for talents to return to their hometowns to start businesses, promote the popularization of Internet finance in rural areas, and adjust the agricultural product trade structure to achieve the common development of various types of crops

An Improved Local Community Detection Algorithm Using Selection Probability

In order to find the structure of local community more effectively, we propose an improved local community detection algorithm ILCDSP, which improves the node selection strategy, and sets selection probability value for every candidate node. ILCDSP assigns nodes with different selection probability values, which are equal to the degree of the nodes to be chosen. By this kind of strategy, the proposed algorithm can detect the local communities effectively, since it can ensure the best search direction and avoid the local optimal solution. Various experimental results on both synthetic and real networks demonstrate that the quality of the local communities detected by our algorithm is significantly superior to the state-of-the-art methods

Lifting load monitoring of mine hoist through vibration signal analysis with variational mode decomposition

Mine hoists play a crucial role in vertical-shaft transportation, and one of the main causes of their faults is abnormal lifting load. However, direct measurement of the load value is difficult. Further, the original structure must be destroyed for sensor installation. To facilitate efficient and accurate monitoring of the lifting load of mine hoist, this paper presents a novel condition-monitoring method based on variational mode decomposition (VMD) and support vector machine (SVM) through vibration signal analysis. First, traditional empirical mode decomposition (EMD) is used to analyze the vibration signal collected by an acceleration sensor, and the number of obtained intrinsic mode functions (IMFs) is employed to set the VMD mode number. Second, the obtained vibration signal is processed by the parameterized VMD, and the useful IMFs of VMD are selected through correlation analysis for feature extraction. Third, the obtained features are used to train an SVM model, and the trained SVM is used to monitor the mine-hoist lifting load. In this study, experiments on an operated mine hoist are also conducted to verify the reliability and validity of the proposed method. The experimental results show that the proposed method can accurately identify the considered lifting load conditions

Rational design and SERS properties of side-by-side, end-to-end and end-to-side assemblies of Au nanorods

By taking advantage of the anisotropy of AuNRs, we design different bifunctional PEG molecules to selectively bind to either the end or side face and simultaneously protect other faces of individual AuNRs. In this way, we successfully achieve orientation-controllable assemblies of AuNRs into side-by-side (SS), end-to-end (EE) and end-to-side (ES) orientations based on the electrostatic interaction between carboxylic PEG and CTAB capping on AuNRs. Furthermore, we find that the different orientations of assembledmotifs in these three types of AuNRs assemblies exhibited different near field coupling between the surface plasma of the neighboring AuNRs, leading to different surface-enhanced Raman signals. Undoubtedly, the current rational design of oriented assembly can be potentially useful for directing anisotropic nanoparticles into well-defined orientations, which provides a powerful route in designing families of novel nanodevices and nanomaterials with programmable electrical and optical properties.National Natural Science Foundation of China[20725310, 90923042]; Research Fund for the Doctoral Program of Higher Education of China[20100121120038]; Natural Science Foundation of Fujian Province of China[2010J01046]; Fundamental Research Funds for the Central Universities[2010121023]; key laboratory of Biomedical Material of Tianji

Greening China naturally

Author Posting. © The Author(s), 2011. This is the author's version of the work. It is posted here by permission of Springer for personal use, not for redistribution. The definitive version was published in AMBIO: A Journal of the Human Environment 40 (2011): 828-831, doi:10.1007/s13280-011-0150-8.China leads the world in afforestation, and is one of the few countries whose forested area is increasing. However, this massive ‘‘greening’’ effort has been less effective than expected; afforestation has sometimes produced unintended environmental, ecological, and socioeconomic consequences, and has failed to achieve the desired ecological benefits. Where afforestation has succeeded, the approach was tailored to local environmental conditions. Using the right plant species or species composition for the site and considering alternatives such as grassland restoration have been important success factors. To expand this success, government policy should shift from a forest-based approach to a results-based approach. In addition, long-term monitoring must be implemented to provide the data needed to develop a cost-effective, scientifically informed restoration policy.This work was supported by the Fundamental Research Funds for the Central Universities (HJ2010-3) and the CAS/ SAFEA International Partnership Program for Creative Research Teams of ‘‘Ecosystem Processes and Services’’

Biological Metaphor, Technological Innovation and Industrial Evolution in Jiangxi Province

From the perspective of the study of biological metaphor analysis, this article intends to explore a "high-quality" regional economic development path that fits the context of the economic transition period. This article first introduces the definition of biological metaphors in evolutionary economics. Second, analyzed the inherent logic of the biological metaphor of technological innovation and industrial evolution, and found that the concept of green development must be integrated into the technological innovation process of individual enterprises at the current stage in order to achieve the transformation and upgrading of the current industrial structure. Third, taking Jiangxi Province as an example, the theoretical framework of biological metaphors for creating a beautiful "Jiangxi model" in China was discussed, and the current state of industrial evolution, technological innovation, and environmental regulation in Jiangxi Province from 2011 to 2017 were described in detail. The rationality of the theoretical framework for the analysis of biological metaphors to create a beautiful Jiangxi model in China, that is, it is suitable for the needs of industrial development in Jiangxi Province. Finally, corresponding policy recommendations are given based on the relevant conclusions

Further results on constructions of generalized bent Boolean functions

National Natural Science Foundation of China (Grant Nos. 61303263, 61309034)Fundamental Research Funds for the Central Universities (Grant No. 2015XKMS086)China Postdoctoral Science Foundation Funded Project (Grant No. 2015T80600)National Natural Science Foundation of China (Grant Nos. 61303263, 61309034)Fundamental Research Funds for the Central Universities (Grant No. 2015XKMS086)China Postdoctoral Science Foundation Funded Project (Grant No. 2015T80600

Multiple Kernel Spectral Regression for Dimensionality Reduction

Traditional manifold learning algorithms, such as locally linear embedding, Isomap, and Laplacian eigenmap, only provide the embedding results of the training samples. To solve the out-of-sample extension problem, spectral regression (SR) solves the problem of learning an embedding function by establishing a regression framework, which can avoid eigen-decomposition of dense matrices. Motivated by the effectiveness of SR, we incorporate multiple kernel learning (MKL) into SR for dimensionality reduction. The proposed approach (termed MKL-SR) seeks an embedding function in the Reproducing Kernel Hilbert Space (RKHS) induced by the multiple base kernels. An MKL-SR algorithm is proposed to improve the performance of kernel-based SR (KSR) further. Furthermore, the proposed MKL-SR algorithm can be performed in the supervised, unsupervised, and semi-supervised situation. Experimental results on supervised classification and semi-supervised classification demonstrate the effectiveness and efficiency of our algorithm