Spatial Patterns and Driving Factors of Urban Residential Embedded Carbon Emissions: An Empirical Study in Kaifeng, China

With the continuous improvement in living standards and great changes in lifestyles, more attention is being paid to the embedded carbon emissions produced by human consumption. With large sample data and high-resolution remote sensing images, we explored the spatial differentiation and influencing factors of household embedded carbon emissions within the city fine scale using the EIO-LCA model, spatial autocorrelation analysis and standard deviation ellipse, quantile regression, etc. The results indicate that the spatial dependence is more obvious than the characteristics of spatial heterogeneity;the high-value area of household embedded carbon emissions gathers in new development zones in cities that are expanding rapidly, mainly with residents in large number of newly-built commercial housing families and the relative’s courtyard of institutions. The factors of family characteristics, housing characteristics, lifestyles, and consumption concept have significant effects on the embedded carbon emissions of each person. The influencing intensity of most factors showed an increasing trend with increased carbon emissions. The study verified the impact of urban sprawl on residential carbon emissions and the applicability of the situated lifestyles theory in the construction of urban low-carbon communities in China.

Design of Remote Control System of Intelligent Community Based on Embedded Web Server

A multichannel remote control system for imelligent community based on the STC89C54 chip was designed with the technique of embedded Web server. The control system can monitor 255 signals and eight control signals of one node at the same time, and can be connected to the internet by the TCP/IP protocol. So the field control information can be shown dynamically in a remote computer by way of web pages. The system has high convenience and friendly monitoring interface, then especially is fit for the large conamunity and storage that need multipoint monitoring and frequent switching door.

Finding Communities by Decomposing and Embedding Heterogeneous Information Network

Community discovery is an important task in social network analysis.However,most existing methods for community discovery rely on the topological structure alone.These methods ignore the rich information available in the content data.In order to solve this issue,in this paper,we present a community discovery method based on heterogeneous information network decomposition and embedding.Unlike traditional methods,our method takes into account topology,node content and edge content,which can supply abundant evidence for community discovery.First,an embedding-based similarity evaluation method is proposed,which decomposes the heterogeneous information network into several subnetworks,and extracts their potential deep representation to evaluate the similarities between nodes.Second,a bottom-up community discovery algorithm is proposed.Via leader nodes selection,initial community generation,and community expansion,communities can be found more efficiently.Third,some incremental maintenance strategies for the changes of networks are proposed.We conduct experimental studies based on three real-world social networks.Experiments demonstrate the effectiveness and the efficiency of our proposed method.Compared with the traditional methods,our method improves normalized mutual information(NMI)and the modularity by an average of 12%and 37%respectively.

Identifying influential nodes in social networks via community structure and influence distribution difference

This paper aims to effectively solve the problem of the influence maximization in social networks.For this purpose,an influence maximization method that can identify influential nodes via the community structure and the influence distribution difference is proposed.Firstly,the network embedding-based community detection approach is developed,by which the social network is divided into several high-quality communities.Secondly,the solution of influence maximization is composed of the candidate stage and the greedy stage.The candidate stage is to select candidate nodes from the interior and the boundary of each community using a heuristic algorithm,and the greedy stage is to determine seed nodes with the largest marginal influence increment from the candidate set through the sub-modular property-based Greedy algorithm.Finally,experimental results demonstrate the superiority of the proposed method compared with existing methods,from which one can further find that our work can achieve a good tradeoff between the influence spread and the running time.

Modularity-based representation learning for networks

Network embedding aims at learning low-dimensional representation of vertexes in a network and effectively preserving network structures.These representations can be used as features for many complex tasks on networks such as community detection and multi-label classification.Some classic methods based on the skip-gram model have been proposed to learn the representation of vertexes.However,these methods do not consider the global structure(i.e.,community structure)while sampling vertex sequences in network.To solve this problem,we suggest a novel sampling method which takes community information into consideration.It first samples dense vertex sequences by taking advantage of modularity function and then learns vertex representation by using the skip-gram model.Experimental results on the tasks of community detection and multi-label classification show that our method outperforms three state-of-the-art methods on learning the vertex representations in networks.

Improved Density Peaking Algorithm for Community Detection Based on Graph Representation Learning

There is a large amount of information in the network data that we canexploit. It is difficult for classical community detection algorithms to handle network data with sparse topology. Representation learning of network data is usually paired with clustering algorithms to solve the community detection problem.Meanwhile, there is always an unpredictable distribution of class clusters outputby graph representation learning. Therefore, we propose an improved densitypeak clustering algorithm (ILDPC) for the community detection problem, whichimproves the local density mechanism in the original algorithm and can betteraccommodate class clusters of different shapes. And we study the communitydetection in network data. The algorithm is paired with the benchmark modelGraph sample and aggregate (GraphSAGE) to show the adaptability of ILDPCfor community detection. The plotted decision diagram shows that the ILDPCalgorithm is more discriminative in selecting density peak points compared tothe original algorithm. Finally, the performance of K-means and other clusteringalgorithms on this benchmark model is compared, and the algorithm is proved tobe more suitable for community detection in sparse networks with the benchmarkmodel on the evaluation criterion F1-score. The sensitivity of the parameters ofthe ILDPC algorithm to the low-dimensional vector set output by the benchmarkmodel GraphSAGE is also analyzed.

Research on the Small - scale and Multi - functional Community Endowment Model

Pension as a part of＂Healthy China＂ has been elevated to the national strategic level. Our country has entered the aging period of rapid development. China is ＂running＂ into an aging society, aging population growth to enhance the pension demand, China＇s current pension model development difficult, difficult to cope with the rapidly growing demand for retirement. Therefore, the establishment of a comprehensive, multi-functional new facility after all, a community of embedded retirement pension to meet the requirements of sustainable development options. The Japanese government＇s main push small-scale multi-function Pension House is a good example of pension services, its mode of operation consistent with China＇s National Day. Thus, summarizing and analyzing the experience of Japan, while the status quo of the development of social pension, to explore a Chinese-style pension service model of innovation. To resolve some of the difficulties of the current development of China＇s current pension model, led by China＇s pension services out of the woods.

Learning distributed representations for community search using node embedding

1 Introduction and main contributions Community search is a query-dependent variant of community detection problem in social network analysis. Algorithms of this type usually start with a query node preknown to be in the target community, and uncover the remaining nodes in the community. The key challenge in this problem is how to find a proper way to represent network structure as a representation that can be easily exploited by downstream data mining models.