A new evolutional model for institutional field knowledge flow network

Purpose:This paper aims to address the limitations in existing research on the evolution of knowledge flow networks by proposing a meso-level institutional field knowledge flow network evolution model(IKM).The purpose is to simulate the construction process of a knowledge flow network using knowledge organizations as units and to investigate its effectiveness in replicating institutional field knowledge flow networks.Design/Methodology/Approach:The IKM model enhances the preferential attachment and growth observed in scale-free BA networks,while incorporating three adjustment parameters to simulate the selection of connection targets and the types of nodes involved in the network evolution process Using the PageRank algorithm to calculate the significance of nodes within the knowledge flow network.To compare its performance,the BA and DMS models are also employed for simulating the network.Pearson coefficient analysis is conducted on the simulated networks generated by the IKM,BA and DMS models,as well as on the actual network.Findings:The research findings demonstrate that the IKM model outperforms the BA and DMS models in replicating the institutional field knowledge flow network.It provides comprehensive insights into the evolution mechanism of knowledge flow networks in the scientific research realm.The model also exhibits potential applicability to other knowledge networks that involve knowledge organizations as node units.Research Limitations:This study has some limitations.Firstly,it primarily focuses on the evolution of knowledge flow networks within the field of physics,neglecting other fields.Additionally,the analysis is based on a specific set of data,which may limit the generalizability of the findings.Future research could address these limitations by exploring knowledge flow networks in diverse fields and utilizing broader datasets.Practical Implications:The proposed IKM model offers practical implications for the construction and analysis of knowledge flow networks within institutions.It provides a valuable tool for understanding and managing knowledge exchange between knowledge organizations.The model can aid in optimizing knowledge flow and enhancing collaboration within organizations.Originality/value:This research highlights the significance of meso-level studies in understanding knowledge organization and its impact on knowledge flow networks.The IKM model demonstrates its effectiveness in replicating institutional field knowledge flow networks and offers practical implications for knowledge management in institutions.Moreover,the model has the potential to be applied to other knowledge networks,which are formed by knowledge organizations as node units.

Generation of scale-free knowledge network with local world mechanism

In order to simulate the real growing process, a new type of knowledge network growth mechanism based on local world connectivity is constructed. By the mean-field method, theoretical prediction of the degree distribution of the knowledge network is given, which is verified by Matlab simulations. When the new added node＇s local world size is very small, the degree distribution of the knowledge network approximately has the property of scale-free. When the new added node＇s local world size is not very small, the degree distribution transforms from pure power-law to the power-law with an exponential tailing. And the scale-free index increases as the number of new added edges decreases and the tunable parameters increase. Finally, comparisons of some knowledge indices in knowledge networks generated by the local world mechanism and the global mechanism are given. In the long run, compared with the global mechanism, the local world mechanism leads the average knowledge levels to slower growth and brings homogenous phenomena.

Research on Network Cognition Model and Mechanism of Intelligent Information Network

Intellectualization has been an inevitable trend in the information network,allowing the network to achieve the capabilities of self-learning,self-optimization,and self-evolution in the dynamic environment.Due to the strong adaptability to the environment,the cognitive theory methods from psychology gradually become an excellent approach to construct the intelligent information network(IIN),making the traditional definition of the intelligent information network no longer appropriate.Moreover,the thinking capability of existing IINs is always limited.This paper redefines the intelligent information network and illustrates the required properties of the architecture,core theory,and critical technologies by analyzing the existing intelligent information network.Besides,we innovatively propose a novel network cognition model with the network knowledge to implement the intelligent information network.The proposed model can perceive the overall environment data of the network and extract the knowledge from the data.As the model’s core,the knowledge guides the model to generate the optimal decisions adapting to the environmental changes.At last,we present the critical technologies needed to accomplish the proposed network cognition model.

Networked Knowledge and Complex Networks:An Engineering View

Along with the development of information technologies such as mobile Internet,information acquisition technology,cloud computing and big data technology,the traditional knowledge engineering and knowledge-based software engineering have undergone fundamental changes where the network plays an increasingly important role.Within this context,it is required to develop new methodologies as well as technical tools for network-based knowledge representation,knowledge services and knowledge engineering.Obviously,the term“network”has different meanings in different scenarios.Meanwhile,some breakthroughs in several bottleneck problems of complex networks promote the developments of the new methodologies and technical tools for network-based knowledge representation,knowledge services and knowledge engineering.This paper first reviews some recent advances on complex networks,and then,in conjunction with knowledge graph,proposes a framework of networked knowledge which models knowledge and its relationships with the perspective of complex networks.For the unique advantages of deep learning in acquiring and processing knowledge,this paper reviews its development and emphasizes the role that it played in the development of knowledge engineering.Finally,some challenges and further trends are discussed.

New Knowledge Network Evaluation Method for Design Rationale Management

Current design rationale （DR） systems have not demonstrated the value of the approach in practice since little attention is put to the evaluation method of DR knowledge. To systematize knowledge management process for future computer-aided DR applications, a prerequisite is to provide the measure for the DR knowledge. In this paper, a new knowledge network evaluation method for DR management is presented. The method characterizes the DR knowledge value from four perspectives, namely, the design rationale structure scale, association knowledge and reasoning ability, degree of design justification support and degree of knowledge representation conciseness. The DR knowledge comprehensive value is also measured by the proposed method. To validate the proposed method, different style of DR knowledge network and the performance of the proposed measure are discussed. The evaluation method has been applied in two realistic design cases and compared with the structural measures. The research proposes the DR knowledge evaluation method which can provide object metric and selection basis for the DR knowledge reuse during the product design process. In addition, the method is proved to be more effective guidance and support for the application and management of DR knowledge.

Modeling and Robustness of Knowledge Network in Supply Chain

The growth and evolution of the knowledge network in supply chain can be characterized by dynamic growth clustering and non-homogeneous degree distribution.The networks with the above characteristics are also known as scale-free networks.In this paper,the knowledge network model in supply chain is established,in which the preferential attachment mechanism based on the node strength is adopted to simulate the growth and evolution of the network.The nodes in the network have a certain preference in the choice of a knowledge partner.On the basis of the network model,the robustness of the three network models based on different preferential attachment strategies is investigated.The robustness is also referred to as tolerances when the nodes are subjected to random destruction and malicious damage.The simulation results of this study show that the improved network has higher connectivity and stability.

Structure of Chinese City Network as Driven by Technological Knowledge Flows

Based on patent cooperation data,this study used a range of city network analysis approaches in order to explore the structure of the Chinese city network which is driven by technological knowledge flows.The results revealed the spatial structure,composition structure,hierarchical structure,group structure,and control structure of Chinese city network,as well as its dynamic factors.The major findings are:1) the spatial pattern presents a diamond structure,in which Wuhan is the central city;2) although the invention patent knowledge network is the main part of the broader inter-city innovative cooperation network,it is weaker than the utility model patent;3) as the senior level cities,Beijing,Shanghai and the cities in the Zhujiang(Pearl) River Delta Region show a strong capability of both spreading and controlling technological knowledge;4) whilst a national technology alliance has preliminarily formed,regional alliances have not been adequately established;5) even though the cooperation level amongst weak connection cities is not high,such cities still play an important role in the network as a result of their location within ′structural holes′ in the network;and 6) the major driving forces facilitating inter-city technological cooperation are geographical proximity,hierarchical proximity and technological proximity.

Structures and dynamics of scientific knowledge networks:An empirical analysis based on a co-word network

Co-word networks are constructed with author-provided keywords in academic publications and their relations of co-occurrence.As special form of scientific knowledge networks,they represent the cognitive structure of scientific literature.This paper analyzes the complex structure of a co-word network based on 8,190 author-provided keywords extracted from 3,651 papers in five Chinese core journals in the field of management science.Small-world and scale-free phenomena are found in this network.A large-scale co-word network graph,which consists of one major giant component and many small isolated components,has been generated with the GUESS software.The dynamic growth of keywords and keyword co-occurrence relationships are described with four new informetrics measures.The results indicate that existing concepts always serve as the intellectual base of new ideas as represented by keywords.

Spread spectrum of knowledge network based on information integration environment

The concept and the characteristic of spread spectrum technology of knowledge network based on information integration environment are given at first. And the basic principle of the wireless network of knowledge and information is recommended. Then a project alternatives using the spread spectrum technology and wireless network is given to build a across-the abroad knowledge network system.

Study on the Interior Market Network Structure and Its Efficiency in a Knowledge Based Company

A knowledge based company is the microcosmic foundation of the knowledge economy, the design of its organization structure should amplify the company competence to be agile to the knowledge elements. This paper expounds an interior market network structure which is fit for the company intellectual capital operation, and analyses this organization pattern about the reasons of existence, the effectiveness of growing up in scale, the economies of knowledge distribution and the efficiency of operation, and it will provide some beneficial theoretical guidance about how can a company improve its competition competence in the knowledge environment through organization innovation.

Artificial Neural Network Method Based on Expert Knowledge and Its Application to Quantitative Identification of Potential Seismic Sources

In this paper,an approach is developed to optimize the quality of the training samples in the conventional Artificial Neural Network(ANN)by incorporating expert knowledge in the means of constructing expert-rule samples from rules in an expert system,and through training by using these samples,an ANN based on expert-knowledge is further developed.The method is introduced into the field of quantitative identification of potential seismic sources on the basis of the rules in an expert system.Then it is applied to the quantitative identification of the potential seismic sources in Beijing and its adjacent area.The result indicates that the expert rule based on ANN method can well incorporate and represent the expert knowledge in the rules in an expert system,and the quality of the samples and the efficiency of training and the accuracy of the result are optimized.

Decision Model of Knowledge Transfer in Big Data Environment

A decision model of knowledge transfer is presented on the basis of the characteristics of knowledge transfer in a big data environment.This model can determine the weight of knowledge transferred from another enterprise or from a big data provider.Numerous simulation experiments are implemented to test the efficiency of the optimization model.Simulation experiment results show that when increasing the weight of knowledge from big data knowledge provider,the total discount expectation of profits will increase,and the transfer cost will be reduced.The calculated results are in accordance with the actual economic situation.The optimization model can provide useful decision support for enterprises in a big data environment.

The Yellow FeverPandemic and the Establishment of a Transatlantic Knowledge Network for Epidemic Prevention in the Late 18th and Early 19th Centuries

During the late 18th and early 19th centuries,a series of conflicts erupted in the Caribbean,leading to the spread of yellow fever to North America and Europe.This yellow fever epidemic was aggravated by war,migration,trade,and other human behaviors,resulting in a decadelong transatlantic pandemic.Groups of physicians in Europe and the United States established a transatlantic network focused on epidemic prevention,to investigate the pathology,causes,and treatments of yellow fever Subsequently,some consular officers were also concerned about the yellow fever epidemic,which led to the expansion of this network.The formation and expansion of the transatlantic knowledge network profoundly demonstrated the spirit of transnationalism and promotes progress in international public health.It sets a precedent for international health cooperation.However,this network was dominated by the so-called"white elite",with European and American countries holding the knowledge hegemony,it had a clear racist and colonialism feature.

A Meta-Analysis of Pension Models for Patients with Mental Disorders

Objective:To analyze the pension models available for patients with mental disorders and design a more suitable one.Methods:A total of 135 pieces of literature in the database of China National Knowledge Infrastructure(CNKI)published from August 11,1970,to November 17,2022,were classified and analyzed.A knowledge map was drawn and the research context was sorted out from the aspects of temporal distribution,spatial distribution,research hotspots,and evolutionary trend,so as to reveal the research status and development trend in the field of pension for patients with mental disorders.Results:The temporal distribution of the literature in this review involved 20 disciplines,41 papers,2 information articles,40 authors,13 research levels,and 20 research institutions.In terms of research hotspots and evolutionary trends,the keywords“disability pension,”“pension institutions,”and“patients with mental disorders”play a fundamental role in the dynamic evolution and diversification of research topics in the field of the mental disorder pension model.Conclusion:There has been not much research on elderly care for patients with mental disorders,and it is still in the exploratory stage without a sustainable and stable research theme.In recent years,keywords such as“the combination of medical care,”“community care for the elderly,”and“intelligent care”for the elderly have become prominent and the number of related studies has increased,and the research quality in this field has also improved.Intelligent medical care for elderly patients with mental disorders will become the trend of future research.

Adaptive and augmented active anomaly detection on dynamic network traffic streams

Active anomaly detection queries labels of sampled instances and uses them to incrementally update the detection model,and has been widely adopted in detecting network attacks.However,existing methods cannot achieve desirable performance on dynamic network traffic streams because(1)their query strategies cannot sample informative instances to make the detection model adapt to the evolving stream and(2)their model updating relies on limited query instances only and fails to leverage the enormous unlabeled instances on streams.To address these issues,we propose an active tree based model,adaptive and augmented active prior-knowledge forest(A3PF),for anomaly detection on network trafic streams.A prior-knowledge forest is constructed using prior knowledge of network attacks to find feature subspaces that better distinguish network anomalies from normal traffic.On one hand,to make the model adapt to the evolving stream,a novel adaptive query strategy is designed to sample informative instances from two aspects:the changes in dynamic data distribution and the uncertainty of anomalies.On the other hand,based on the similarity of instances in the neighborhood,we devise an augmented update method to generate pseudo labels for the unlabeled neighbors of query instances,which enables usage of the enormous unlabeled instances during model updating.Extensive experiments on two benchmarks,CIC-IDS2017 and UNSW-NB15,demonstrate that A3PF achieves significant improvements over previous active methods in terms of the area under the receiver operating characteristic curve(AUC-ROC)(20.9%and 21.5%)and the area under the precision-recall curve(AUC-PR)(44.6%and 64.1%).

Network Meets ChatGPT:Intent Autonomous Management,Control and Operation

Telecommunication has undergone significant transformations due to the continuous advancements in internet technology,mobile devices,competitive pricing,and changing customer preferences.Specifically,the most recent iteration of OpenAI’s large language model chat generative pre-trained transformer(ChatGPT)has the potential to propel innovation and bolster operational performance in the telecommunications sector.Nowadays,the exploration of network resource management,control,and operation is still in the initial stage.In this paper,we propose a novel network artificial intelligence architecture named language model for network traffic(NetLM),a large language model based on a transformer designed to understand sequence structures in the network packet data and capture their underlying dynamics.The continual convergence of knowledge space and artificial intelligence(AI)technologies constitutes the core of intelligent network management and control.Multi-modal representation learning is used to unify the multi-modal information of network indicator data,traffic data,and text data into the same feature space.Furthermore,a NetLM-based control policy generation framework is proposed to refine intent incrementally through different abstraction levels.Finally,some potential cases are provided that NetLM can benefit the telecom industry.

Sustainable regional development through knowledge networks:Review of case studies

This study focuses on the importance of knowledge management in the process of development.The goal is to build a regional knowledge network for regional sustainable improvement.The method used in this study stands on two theoretical and experimental balks.The study also provides feasible outcomes by suggesting a model for knowledge-based cities.The model assists regional/urban planners in managing knowledge productions,organizing regional knowledge institutions,and developing cities by utilizing the advantages of the network.Results of this applied research support the creation of knowledge networks in similar cities.

The Frame of Knowledge-networking Capability in Virtual Rnterprise and Its Promotion Strategy

Based on Virtual Enterprise （VE）, the knowledge networking capacity （KNC） is researched. The knowledge networking capability is divided into four dimensions, including planning capability, building capabil ity, running capability and updating capability, by analyzing the challenge to the knowledge network. Then, atheoretical framework and a dynamic system are built to the knowledge network and promote a great innovation in virtual enterprise by knowledge flow. farthermore, enhance the strategy of improving knowledge networking ca pacity of virtual enterprise to help the enterprise handle the problem of knowledge network better is proposed. hancing knowledge network, thereby, facilitates virtual enterprise to adapt the market need.

Scientometric analysis of research trends on solid oxide electrolysis cells for green hydrogen and syngas production

Solid oxide electrolysis cell(SOEC)is a promising water electrolysis technology that produces hydrogen or syngas through water electrolysis or water and carbon dioxide co-electrolysis.Green hydrogen or syngas can be produced by SOEC with renewable energy.Thus,SOEC has attracted continuous attention in recent years for the urgency of developing environmentally friendly energy sources and achieving carbon neutrality.Focusing on 1276 related articles retrieved from the Web of Science(WoS)database,the historical development of SOECs are depicted from 1983 to 2023 in this paper.The co-occurrence networks of the countries,source journals,and author keywords are generated.Moreover,three main clusters showing different content of the SOEC research are identified and analyzed.Furthermore,the scientometric analysis and the content of the high-cited articles of the research of different topics of SOECs:fuel electrode,air electrode,electrolyte,co-electrolysis,proton-conducting SOECs,and the modeling of SOECs are also presented.The results show that co-electrolysis and proton-conducting SOECs are two popular directions in the study of SOECs.This paper provides a straightforward reference for researchers interested in the field of SOEC research,helping them navigate the landscape of this area of study,locate potential partners,secure funding,discover influential scholars,identify leading countries,and access key research publications.