论“关系”网络中的社会资本——一个中西方社会网络比较分析的视角

与西方"规则"导向的社会网络不同,华人社会的联结以"关系"为导向。"关系"网络是华人获取社会资本的重要途径。本文从中西方社会网络比较的视角出发,研究了"关系"网络中的社会资本问题。首先在个体中心网(ego networks)中分析了源于儒家伦理的"人情-面子"关系,认为其功能类似于"信用卡"、"保险费"或"荣誉证书",具有情感性目的与工具性目的难以区分、交往规则随感情深浅变化、特殊主义"声誉"机制等特征。然后在整体社会网(social networks)中分析了"圈内"、"圈外"正、负社会资本的存在、运作机理和竞争性优劣势。最后分别从个体和群体层面提出了提升"关系"网络中正社会资本、减少负社会资本的若干建议。

Intelligent method to develop constitutive relationship of Ti-6Al-2Zr-1Mo-1V alloy

The isothermal compression tests were carried out in the Thermecmastor-Z thermo-simulator at temperatures of 800, 850, 900, 950, 1000 and 1050 ℃ and the strain rates of 0.01, 0.1, 1 and 10 s-1. The influence of deformation temperature and strain rate on the flow stress of Ti-6Al-2Zr-IMo-IV alloy was studied. Based on the experimental data sets, the high temperature deformation behavior of Ti-6A1-2Zr-IMo-IV alloy was presented using the intelligent method of artificial neural network （ANN）. The results indicate that the predicted flow stress values by ANN model is quite consistent with the experimental results, which implies that the artificial neural network is an effective tool for studying the hot deformation behavior of the present alloy. In addition, the development of graphical user interface is implemented using Visual Basic programming language.

Decentralized Control Based on FNNSMC for Interconnected Uncertain Nonlinear Systems

A new type controller, fuzzy neural networks sliding mode controller (FNNSMC), is developed for a class of large scale systems with unknown bounds of high order interconnections and disturbances. Although sliding mode control is simple and insensitive to uncertainties and disturbances, there are two main problems in the sliding mode controller (SMC): control input chattering and the assumption of known bounds of uncertainties and disturbances. The FNNSMC, which incorporates the fuzzy neural networks (FNN) and the SMC, can eliminate the chattering by using the continuous output of the FNN to replace the discontinuous sign term in the SMC. The bounds of uncertainties and disturbances are also not required in the FNNSMC design. The simulation results show that the FNNSMC has more robustness than the SMC.

Mining Topical Influencers Based on the Multi-Relational Network in Micro-Blogging Sites

In micro-blogging contexts such as Twitter,the number of content producers can easily reach tens of thousands,and many users can participate in discussion of any given topic.While many users can introduce diversity,as not all users are equally influential,it makes it challenging to identify the true influencers,who are generally rated as being interesting and authoritative on a given topic.In this study,the influence of users is measured by performing random walks of the multi-relational data in micro-blogging:retweet,reply,reintroduce,and read.Due to the uncertainty of the reintroduce and read operations,a new method is proposed to determine the transition probabilities of uncertain relational networks.Moreover,we propose a method for performing the combined random walks for the multi-relational influence network,considering both the transition probabilities for intra-and inter-networking.Experiments were conducted on a real Twitter dataset containing about 260 000 users and 2.7million tweets,and the results show that our method is more effective than TwitterRank and other methods used to discover influencers.

Global optimization by small-world optimization algorithm based on social relationship network

A fast global convergence algorithm, small-world optimization (SWO), was designed to solve the global optimization problems, which was inspired from small-world theory and six degrees of separation principle in sociology. Firstly, the solution space was organized into a small-world network model based on social relationship network. Secondly, a simple search strategy was adopted to navigate into this network in order to realize the optimization. In SWO, the two operators for searching the short-range contacts and long-range contacts in small-world network were corresponding to the exploitation and exploration, which have been revealed as the common features in many intelligent algorithms. The proposed algorithm was validated via popular benchmark functions and engineering problems. And also the impacts of parameters were studied. The simulation results indicate that because of the small-world theory, it is suitable for heuristic methods to search targets efficiently in this constructed small-world network model. It is not easy for each test mail to fall into a local trap by shifting into two mapping spaces in order to accelerate the convergence speed. Compared with some classical algorithms, SWO is inherited with optimal features and outstanding in convergence speed. Thus, the algorithm can be considered as a good alternative to solve global optimization problems.

Relationships Between Fractal Road and Drainage Networks in Wuling Mountainous Area:Another Symmetric Understanding of Human-Environment Relations

Symmetrical relationships between humans and their environment have been referred to as an extension of symmetries in the human geographical system and have drawn great attention. This paper explored the symmetry between physical and human systems through fractal analysis of the road and drainage networks in Wuling mountainous area. We found that both the road and drainage networks reflect weak clustering distributions. The evolution of the road network shared a significant self-organizing composition, while the drainage network showed obvious double fraetal characteristics. The geometric fractal dimension of the road network was larger than that of the drainage network. In addition, when assigned a weight relating to hierarchy or length, neither the road network nor drainage network showed a fractal property. These findings indicated that the fractal evolution of the road network shared certain similarities with fractal distribution of the drainage network. The symmetry between the two systems resulted from an interactive process of destroying symmetry at the lower order and reconstructing symmetry at the higher order. The relationships between the fractal dimensions of the rural-urban road network, the drainage network andthe urban system indicated that the development of this area was to achieve the symmetrical isomorphism of physical-human geographical systems.

Dependency-based importance measures of components in mechatronic systems with complex network theory

To compensate for the limitations of previous studies,a complex network-based method is developed for determining importance measures,which combines the functional roles of the components of a mechatronic system and their topological positions.First,the dependencies among the components are well-represented and well-calculated.Second,a mechatronic system is modeled as a weighted and directional functional dependency network(FDN),in which the node weights are determined by the functional roles of components in the system and their topological positions in the complex network whereas the edge weights are represented by dependency strengths.Third,given that the PageRank algorithm cannot calculate the dependency strengths among components,an improved PageRank importance measure(IPIM)algorithm is proposed,which combines the node weights and edge weights of complex networks.IPIM also considers the importance of neighboring components.Finally,a case study is conducted to investigate the accuracy of the proposed method.Results show that the method can effectively determine the importance measures of components.

QoE-Driven Social Aware Caching Placement for Terrestrial-Satellite Networks

In remote terrestrial-satellite networks, caching is a very promising technique to alleviate the burden of space cloudlet(e.g., cache-enabled satellite user terminal) and to improve subscribers' quality of experience(Qo E) in terms of buffering delay and achievable video streaming rate. In this paper, we studied a Qo E-driven caching placement optimization problem for video streaming that takes into account the required video streaming rate and the social relationship among users. Social ties between users are used to designate a set of helpers with caching capability, which can cache popular files proactively when the cloudlet is idle. We model the utility function of Qo E as a logarithmic function. Then, the caching placement problem is formulated as an optimization problem to maximize the user's average Qo E subject to the storage capacity constraints of the helpers and the cloudlets. Furthermore, we reformulate the problem into a monotone submodular optimization problem with a partition matroid constraint, and an efficient greedy algorithm with 1-1 e approximation ratio is proposed to solve it. Simulation results show that the proposed caching placement approach significantly outperforms the traditional approaches in terms of Qo E, while yields about the same delay and hit ratio performance compare to the delay-minimized scheme.

Data Mapping Principles and Algorithm for Self-Configurable Systems

With the challenges brought by the expansion of network scale,as well as the diversity of the equipments and the complexity of network protocols,many self-configurable systems have been proposed combining formal specification and model finding techniques.In this paper,we pay more attention to formal specifications of network information,i.e.,exploring principles and algorithm to map network information(topology,devices and status,etc.) to Alloy specifications.We first model network information in relational form,which is easy to realize because of the structured feature of network information in nature.Then we map the relational data to Alloy specifications according to our novel data mapping principles and algorithm.Based on the transition of relational data,it is possible to automatically map network information to Alloy specifications.We evaluate our data mapping principles and algorithm by applying them to a practical application scenario.The results illustrate that we can find a model for the task within a tolerant time interval,which implies that our novel approach can convert relational data to Alloy specifications correctly and efficiently.

Social network search based on semantic analysis and learning

Because of everyone＇s involvement in social networks, social networks are full of massive multimedia data, and events are got released and disseminated through social networks in the form of multi-modal and multi-attribute heterogeneous data. There have been numerous researches on social network search. Considering the spatio-temporal feature of messages and social relationships among users, we summarized an overall social network search framework from the perspective of semantics based on existing researches. For social network search, the acquisition and representation of spatio-temporal data is the basis, the semantic analysis and modeling of social network cross-media big data is an important component, deep semantic learning of social networks is the key research field, and the indexing and ranking mechanism is the indispensable part. This paper reviews the current studies in these fields, and then main challenges of social network search are given. Finally, we give an outlook to the prospect and further work of social network search.

Exploring relations between city regions based on mobile phone data

City regions often have great diversity in form and function. To better understand the role of each region, the relations between city regions need to be carefully studied. In this work, the human mobility relations between regions of Shanghai based on mobile phone data is explored. By formulating the regions as nodes in a network and the commuting between each pair of regions as link weights, the distribution of nodes degree, and spatial structures of communities in this relation network are studied. Statistics show that regions locate in urban centers and traffic hubs have significantly larger degrees. Moreover, two kinds of spatial structures of communities are found. In most communities, nodes are spatially neighboring. However, in the communities that cover traffic hubs, nodes often locate along corridors.

Graph-based method for human-object interactions detection

Human-object interaction(HOIs)detection is a new branch of visual relationship detection,which plays an important role in the field of image understanding.Because of the complexity and diversity of image content,the detection of HOIs is still an onerous challenge.Unlike most of the current works for HOIs detection which only rely on the pairwise information of a human and an object,we propose a graph-based HOIs detection method that models context and global structure information.Firstly,to better utilize the relations between humans and objects,the detected humans and objects are regarded as nodes to construct a fully connected undirected graph,and the graph is pruned to obtain an HOI graph that only preserving the edges connecting human and object nodes.Then,in order to obtain more robust features of human and object nodes,two different attention-based feature extraction networks are proposed,which model global and local contexts respectively.Finally,the graph attention network is introduced to pass messages between different nodes in the HOI graph iteratively,and detect the potential HOIs.Experiments on V-COCO and HICO-DET datasets verify the effectiveness of the proposed method,and show that it is superior to many existing methods.

Consensus of Multi-Agent Systems with Prestissimo Scale-Free Networks

In this paper, the relations of the network topology and the moving consensus of multi-agent systems are studied. A consensus-prestissimo scale-free network model with the static preferential-consensus attachment is presented on the rewired link of the regular network. The effects of the static preferential-consensus BA network on the algebraic connectivity of the topology graph are compared with the regular network. The robustness gain to delay is analyzed for variable network topology with the same scale. The time to reach the consensus is studied for the dynamic network with and without communication delays. By applying the computer simulations, it is validated that the speed of the convergence of multi-agent systems can be greatly improved in the preferential-consensus BA network model with different configuration.

Response of tilt and strain meters in Hubei province to the 2011 Mw9. 0 Japan earthquake

Tih and strain meters of the deformation-observation network in Hubei Province all responded to the Mw9.0 Japan earthquake on March 11,2011. By analyzing the co-seismic responses,we found that firstly there was essentially a linear correlation between response time and epicentral distance. Secondly, there was some correlation between maximum response amplitude and earthquake magnitude as well as between the duration and earthquake magnitude. Thirdly, the response amplitudes and decay rates were different for different types of instruments. Due to less data-sampling frequency, the deformation instruments, could not display the first motion of P and S waves, but responded mainly to far-field surface waves. Before the earthquake, the NS earthtide component recorded by the cave stainmeter at Yichang was distorted for nearly eight hours. While digital deformation observation did not show complete information about the earthquake source, it still reflected some key features of seismic-wave propagation.

Transition and Transgression of Identity in Chuck Palahniuk＇s Survivor

The post-modernist concerning with identity and subjectivity posits that identity is multi-faceted and constantly gets re-constructed by choice. This results in increasingly fragmented and unstable identities leading to an inevitable breakdown in social relationships and sanity. A limited and dependable set of coherent identities began to fragment into a diverse and unstable series of competing identities. Post-modernism stifled the idea of nation, religion, family, and identity as bedrocks of stability and security. Disillusioned post-modernist world has deconstructed the well-defined boundaries that governed the life, otherwise, transition and transgression through and across these domains became the norm that allowed negotiation in daily lives. Where the subject became a terminal of multiple networks, consciously enacting the repetitions through which identity was getting produced and changed, demonstrating identity＇s ＂performativity＂. Establishing that what we believe to be true is only a construction or an interpretation of reality, shaped by our own socially constructed bias, thereby putting to scrutiny the idea of reality and real itself, which removed any sense of objective reality and with it the notion of an objective self. Identity is constantly changing and transitioning, the idea of the self which is entangled with multiplicity, heterogeneity, difference and ceaselessly becoming caught between the present and future, real and unreal, death and life. This research paper will carry out an analysis on evidential aspects in the selected novel and will further interpret the episodes of transition and transgressions that inform this novel.

Challenges and Possibilities for the Household Medicine Lease （HML） System Viewed in Light of CRM： HML-Based Everyday Self-Medication in Ubiquitous Network Society

Household medicine lease （HML） industry originated way back in the Edo period （17C-19C）, when it was promoted by the local fiefdom government to revitalize the economy. Accumulations of wealth, acquired thereafter from everywhere outside the region, have culminated in the formation of the present-day industrial cluster in Toyama, the largest in the whole area facing the Sea of Japan. Today an adaptation of the quasi-CRM （customer relationship management） business model of the HML system has proved to be a success in Mongolia. This fact seems to offer the authors some clues for dealing with those problems that healthcare and medical services in Japan and elsewhere are riddled with. In this paper, focusing on the common critical success factors （CSFs） behind the success of the authors＇ prototype HML system and its recent successful application in Mongolia, the authors will analyze these factors from the perspective of CRM. The authors will then clarify the following： （1） the usefulness of the business model for ensuring primary healthcare for people in developing countries; （2） the usefulness in our ubiquitous network society of applying ICT to the HML system; （3） possible contributions that the use of the system can make toward improving the quality of our everyday healthcare and medical services in our prominently aging society; and the authors will also suggest （4） the importance of elevating ＂individual self-medication＂ to ＂community-based self-medication＂.

From Transcendence to ＂Altercendence＂？

There seems to be a renewed interest in the last couple decades in studying and writing about consciousness. Consciousness may be the most difficult question humans are trying to resolve. Yet eliciting transcendence, a phenomenon that rises above, or goes beyond, normal limits of consciousness, has been an identified goal for many artists in the past, possibly even in cave art. Drawing on works shown in the 2006 Art Gallery of Hamilton exposition called Sublime Embrace： Experiencing Consciousness in Contemporary Art and in the 2002 Houston Contemporary Art Museum 2002 show called THE INWARD EYE： Transcendence in Contemporary Art, the author will explore the biological, psychological, cultural, and spiritual facets of transcendence. Perhaps, with the renewed symbiotic relationship between the sciences and art, the convergence of digital media, and the emergence of global communication networks, we are at the verge of art that will create a change, an alteration in our consciousness, and an altercendent state. What this change will be is yet a mystery.

Estimation of reservoir porosity using probabilistic neural network and seismic attributes

Porosity is one of the most important properties of oil and gas reservoirs. The porosity data that come from well log are only available at well points. It is necessary to use other method to estimate reservoir porosity.Seismic data contain abundant lithological information. Because there are inherent correlations between reservoir property and seismic data,it is possible to estimate reservoir porosity by using seismic data and attributes.Probabilistic neural network is a powerful tool to extract mathematical relation between two data sets. It has been used to extract the mathematical relation between porosity and seismic attributes. Firstly,a seismic impedance volume is calculated by seismic inversion. Secondly,several appropriate seismic attributes are extracted by using multi-regression analysis. Then a probabilistic neural network model is trained to obtain a mathematical relation between porosity and seismic attributes. Finally,this trained probabilistic neural network model is implemented to calculate a porosity data volume. This methodology could be utilized to find advantageous areas at the early stage of exploration. It is also helpful for the establishment of a reservoir model at the stage of reservoir development.

Partner selection of collaborative innovation of strategic emerging industry - based on ANP-GRA

The rise and development of strategic emerging industries need a new innovative mode--collaborative innovation, collaborative innovation as a complex multi-object relationship, it is essential to select an appropriate partner. Partner selection is the key to the success of strategic emerging industry collaborative innovation. This paper takes collaborative innovation partner selection as research focus, establishing evaluation system of collaborative innovation partner selection, using analytic network process （ANP） to determine the weight of evaluation index, then, obtaining grey correlation rank of collaborative innovation partner by Grey relational analysis （GILA）, providing the basis for strategic emerging industry to select collaborative innovation partner.

Correlation methods of base-level cycle based on wavelet neural network

The authors discussed the method of wavelet neural network （WNN） for correlation of base-level cycle. A new vectored method of well log data was proposed. Through the training with the known data set, the WNN can remenber the cycle pattern characteristic of the well log curves. By the trained WNN to identify the cycle pattern in the vectored log data, the ocrrdation process among the well cycles was completed. The application indicates that it is highly efficient and reliable in base-level cycle correlation.