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Scientific knowledge evolution is an important signal for the innovative development of science and technology. As we know, new concepts and ideas are frequently born out of extensive recombination of existing concepts or notions. The evolution of a single knowledge unit or concept can be transformed into the formation of its ego-centered network from the perspective of combination innovation. Specifically, we proposed the eight research hypotheses from three aspects, namely, preferential attachment, transitivity, and homophily mechanisms. The 10,462 egocentric networks of scientific knowledge were extracted from knowledge co-occurrence network (KCN), and the Exponential Random Graph Models (ERGMs) were applied to model these sample networks individually, taking into account the influence of endogenous network structure and exogenous knowledge attribute variables. By conducting large-scale analytics on the fitting results, we found that (1) the degree centrality has a positive effect on knowledge evolution in the 99.9% sample networks, while the clustering coefficient contributes to the knowledge evolution in 56.8% sample networks at the 0.05 significance level; (2) the adoption behavior and domain impact of authors positively influence the scientific knowledge evolution, respectively, in the 93.5% and 80.8% sample networks; and (3) the knowledge type as well as the journal rank has an impact on the knowledge network evolution, demonstrating the homophily mechanism during the evolution of scientific knowledge. [ABSTRACT FROM AUTHOR]

Environmental knowledge networks (EKNs) link research collaborators in a common purpose to produce data and knowledge to better understand social-ecological phenomena and address environmental challenges. Over recent years, as scientists have grappled with how to produce data and actionable knowledge for conservation and sustainability, more EKNs have been established. Although each network is founded for its own purposes and maintains its own goals and ways of operating, these networks are generally managed by scientists to produce knowledge to advance science and decision making. In this Insight article, we articulate key qualities and benefits of EKNs and shows how EKNs can address grand challenges that cannot be answered by a single team or institution, create a diverse, vibrant culture of science and community of practice, and provide innovative solutions and knowledge to society. We also discuss challenges of EKN governance, and how challenges may vary with a network's development. Finally, based on a synthesis of structured discussions about key issues in EKN management, we share recommendations and best practices, emphasizing management practices that are inclusive, reflexive, adaptive, and flexible, so that others may benefit from our experience leading EKNs. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract Background Knowledge networks, such as Communities of Practice (CoP), are essential elements of knowledge management. They play a crucial role in assimilating various knowledge domains and converting individual knowledge into collective knowledge. This study aimed to assess the concept of knowledge networks and identify facilitators and barriers influencing knowledge sharing in infectious diseases, according to Iranian experts. Methods This qualitative study employed content analysis and used purposive and snowball sampling. The data were collected via online or face-to-face interviews with 25 participants with diverse expertise in infectious diseases (both clinical and non-clinical), epidemiology, knowledge management, and knowledge-based business management in Iran. The thematic analysis technique was used to code the interviews, and the collected data were analyzed using MAXQDA 20 software. Results Thematic analysis of the interviews led to 437 codes. These codes were categorized into two groups: facilitators and barriers. The facilitators shaping the knowledge network for infectious diseases were classified into three main categories: individual factors, organizational factors, and communication mechanisms. Individual factors involved two themes: strengthening knowledge exchange between experts in infectious diseases and personal characteristics such as the criteria for network membership. Organizational factors comprised three themes: organizational and trans-organizational factors, management strategies, and interactions with non-governmental sectors. Communication mechanisms included two themes: the use of information technology and knowledge brokers. In addition, three important challenges were identified as barriers influencing the knowledge network: administration and policy-making, organizational and trans-organizational, and personal challenges. Conclusions Several facilitators and barriers influence the formation of an infectious disease knowledge network, which must be addressed to ensure its effectiveness, development, and long-term sustainability. Addressing these factors will enable the network to effectively integrate diverse knowledge and contribute to advancing infectious disease management.

Abstract Background Cancer is a major public health challenge globally. However, little is known about the evolution patterns of cancer research communities and the influencing factors of their research capacity and impact, which is affected not only by the social networks established through research collaboration but also by the knowledge networks in which the research projects are embedded. Methods The focus of this study was narrowed to a specific topic – 'synthetic lethality’ – in cancer research. This field has seen vibrant growth and multidisciplinary collaboration in the past decade. Multi-level collaboration and knowledge networks were established and analysed on the basis of bibliometric data from ‘synthetic lethality’-related cancer research papers. Negative binomial regression analysis was further applied to explore how node attributes within these networks, along with other potential factors, affected paper citations, which are widely accepted as proxies for assessing research capacity and impact. Results Our study revealed that the synthetic lethality-based cancer research field is characterized by a knowledge network with high integration, alongside a collaboration network exhibiting some clustering. We found significant correlations between certain factors and citation counts. Specifically, a leading status within the nation-level international collaboration network and industry involvement were both found to be significantly related to higher citations. In the individual-level collaboration networks, lead authors’ degree centrality has an inverted U-shaped relationship with citations, while their structural holes exhibit a positive and significant effect. Within the knowledge network, however, only measures of structural holes have a positive and significant effect on the number of citations. Conclusions To enhance cancer research capacity and impact, non-leading countries should take measures to enhance their international collaboration status. For early career researchers, increasing the number of collaborators seems to be more effective. University–industry cooperation should also be encouraged, enhancing the integration of human resources, technology, funding, research platforms and medical resources. Insights gained through this study also provide recommendations to researchers or administrators in designing future research directions from a knowledge network perspective. Focusing on unique issues especially interdisciplinary fields will improve output and influence their research work.

Background: Knowledge networks, such as Communities of Practice (CoP), are essential elements of knowledge management. They play a crucial role in assimilating various knowledge domains and converting individual knowledge into collective knowledge. This study aimed to assess the concept of knowledge networks and identify facilitators and barriers influencing knowledge sharing in infectious diseases, according to Iranian experts. Methods: This qualitative study employed content analysis and used purposive and snowball sampling. The data were collected via online or face-to-face interviews with 25 participants with diverse expertise in infectious diseases (both clinical and non-clinical), epidemiology, knowledge management, and knowledge-based business management in Iran. The thematic analysis technique was used to code the interviews, and the collected data were analyzed using MAXQDA 20 software. Results: Thematic analysis of the interviews led to 437 codes. These codes were categorized into two groups: facilitators and barriers. The facilitators shaping the knowledge network for infectious diseases were classified into three main categories: individual factors, organizational factors, and communication mechanisms. Individual factors involved two themes: strengthening knowledge exchange between experts in infectious diseases and personal characteristics such as the criteria for network membership. Organizational factors comprised three themes: organizational and trans-organizational factors, management strategies, and interactions with non-governmental sectors. Communication mechanisms included two themes: the use of information technology and knowledge brokers. In addition, three important challenges were identified as barriers influencing the knowledge network: administration and policy-making, organizational and trans-organizational, and personal challenges. Conclusions: Several facilitators and barriers influence the formation of an infectious disease knowledge network, which must be addressed to ensure its effectiveness, development, and long-term sustainability. Addressing these factors will enable the network to effectively integrate diverse knowledge and contribute to advancing infectious disease management. [ABSTRACT FROM AUTHOR]

Background: Cancer is a major public health challenge globally. However, little is known about the evolution patterns of cancer research communities and the influencing factors of their research capacity and impact, which is affected not only by the social networks established through research collaboration but also by the knowledge networks in which the research projects are embedded. Methods: The focus of this study was narrowed to a specific topic – 'synthetic lethality' – in cancer research. This field has seen vibrant growth and multidisciplinary collaboration in the past decade. Multi-level collaboration and knowledge networks were established and analysed on the basis of bibliometric data from 'synthetic lethality'-related cancer research papers. Negative binomial regression analysis was further applied to explore how node attributes within these networks, along with other potential factors, affected paper citations, which are widely accepted as proxies for assessing research capacity and impact. Results: Our study revealed that the synthetic lethality-based cancer research field is characterized by a knowledge network with high integration, alongside a collaboration network exhibiting some clustering. We found significant correlations between certain factors and citation counts. Specifically, a leading status within the nation-level international collaboration network and industry involvement were both found to be significantly related to higher citations. In the individual-level collaboration networks, lead authors' degree centrality has an inverted U-shaped relationship with citations, while their structural holes exhibit a positive and significant effect. Within the knowledge network, however, only measures of structural holes have a positive and significant effect on the number of citations. Conclusions: To enhance cancer research capacity and impact, non-leading countries should take measures to enhance their international collaboration status. For early career researchers, increasing the number of collaborators seems to be more effective. University–industry cooperation should also be encouraged, enhancing the integration of human resources, technology, funding, research platforms and medical resources. Insights gained through this study also provide recommendations to researchers or administrators in designing future research directions from a knowledge network perspective. Focusing on unique issues especially interdisciplinary fields will improve output and influence their research work. [ABSTRACT FROM AUTHOR]

Collaboration networks are widely recognized as essential channels for accessing innovation resources and facilitating creative activities by enabling the exchange of knowledge and information. However, there is little known about whether and how the similarities and dissimilarities between actors forming ties in a collaboration network can either stimulate or inhibit firms' breakthrough innovation. This study explores the relationship between degree assortativity in collaboration networks and breakthrough innovation performance, considering the moderating role of knowledge network characteristics. Using a sample of 80,129 semiconductor patents from the United States Patent and Trademark Office database spanning the years 1975 to 2007, we constructed both the internal collaboration network and the knowledge network of firms. To test our hypotheses, we employed a negative binomial regression model. Our findings demonstrate that firms with lower degree assortativity in their collaboration networks tend to exhibit higher levels of breakthrough innovation performance compared to those with higher degree assortativity. Moreover, the number of direct ties in the knowledge network strengthens the negative relationship between collaboration network degree assortativity and breakthrough innovation. Conversely, the number of non-redundant ties in the knowledge network mitigates the negative relationship between collaboration network degree assortativity and breakthrough innovation. This study provides practical guidance for firms aiming to enhance their innovation capabilities by simultaneously developing internal collaboration networks and knowledge networks. [ABSTRACT FROM AUTHOR]

BackgroundModerate to severe fatigue occurs in up to 94% of patients with cancer. Recent evidence suggests that morning and evening fatigue are distinct dimensions of physical fatigue. The purposes of this study were to evaluate the transcriptome for common and distinct perturbed inflammatory pathways in patients receiving chemotherapy who reported low versus high levels of morning or low versus high levels of evening cancer-related fatigue.MethodsPatients completed questionnaires during the week prior to their chemotherapy treatment. Severity of morning and evening fatigue was evaluated using the Lee Fatigue Scale. Gene expression and pathway impact analyses (PIA) were performed in two independent samples using RNA-sequencing (n = 357) and microarray (n = 360). Patterns of interactions between and among these perturbed pathways were evaluated using a knowledge network (KN).ResultsAcross the PIA, nine perturbed pathways (FDR

Cyclic peptides offer a range of notable advantages, including potent antibacterial properties, high binding affinity and specificity to target molecules, and minimal toxicity, making them highly promising candidates for drug development. However, a comprehensive database that consolidates both synthetically derived and naturally occurring cyclic peptides is conspicuously absent. To address this void, we introduce CyclicPepedia (https://www.biosino.org/iMAC/cyclicpepedia/), a pioneering database that encompasses 8744 known cyclic peptides. This repository, structured as a composite knowledge network, offers a wealth of information encompassing various aspects of cyclic peptides, such as cyclic peptides' sources, categorizations, structural characteristics, pharmacokinetic profiles, physicochemical properties, patented drug applications, and a collection of crucial publications. Supported by a user-friendly knowledge retrieval system and calculation tools specifically designed for cyclic peptides, CyclicPepedia will be able to facilitate advancements in cyclic peptide drug development. [ABSTRACT FROM AUTHOR]

This study develops a conceptual model of network dynamics on innovation by integrating observations and insights from a world-leading innovation company with concepts and theories from the relevant literature. This study first defines the two stages of the innovation process – early-stage and late-stage and unpacks two types of network activities – knowledge networks and social networks. This work then suggests that the experimental learning cycle with failures spontaneously stimulates network activities to become cognitively activated in the innovation process and incorporates six testable propositions. This study illuminates the idea of Learning by Networking and demonstrates that inventors constitute the reality about what knowledge involves and which people act and intentionally create network dynamics for each task, thus merging these streams as a sequential phenomenon. [ABSTRACT FROM AUTHOR]

Firms’ knowledge characteristics shape their research and development (R&D alliance activities). When denoting the characteristics, the alliance literature has exclusively focused on firms’ internal knowledge characteristics, leaving structural properties of their knowledge relatively unexplored. Therefore, this study aims to investigate how firms’ knowledge network characteristics are associated with the propensity to ally. By using 235,024 unique alliance dyads between U.S. semiconductor firms and their potential partners, we examine the effects of two knowledge network characteristics – degree centrality and structural holes – on alliance formation. We further examine the joint effects of the knowledge network characteristics. Our findings reveal the curvilinear relationship between degree centrality and the likelihood of alliance formation. We also find support for the positive joint effects on the likelihood of alliance formation. These findings provide implications for the varying propensity to ally associated with the knowledge network characteristics, i.e. structural positions of a firm’s knowledge elements in the sector-level knowledge network. [ABSTRACT FROM AUTHOR]

Do forerunners in global value chains (GVCs) select supplier in knowledge collaboration in a different way from latecomers? There are many factors in the selection criteria in co-creating GVC value and enhancing a focal firm's competitiveness. However, little has been revealed on how firms select their GVC suppliers from the knowledge embeddedness perspective. Furthermore, latecomers and forerunners have gaps in knowledge that may result in different weights on the same selection elements. Based on the knowledge embeddedness theory, we compare the selection criteria of a latecomer (Huawei) with a forerunner (Apple). Specifically, we identify 129 Apple or Huawei global suppliers in 2018 and collect 144,496 patents of these suppliers. Using the data of patents and the tracking of backward citations from 2013 to 2017, we find that, as a latecomer, Huawei depends more on criteria of structural hole positions in GVC knowledge network, knowledge base, and scientific knowledge than Apple does. We further conduct scenario analysis to understand Huawei's decoupling possibility from GVCs under tech cold war. [ABSTRACT FROM AUTHOR]

Purpose: In the age of information, the increasing competition among banks to gain a larger share of the monetary and financial market has led to a more detailed and profound focus on the needs and demands of customers. Achieving maximum customer satisfaction initially involves understanding their needs and demands and then translating these into appropriate services. Given the growing complexity of financial and banking systems, this can only be accomplished by establishing codified and systematic methods that transform these concepts into an organizational process. Furthermore, with the evolving needs, demands, and expectations of customers, it is essential to first identify what the customer desires and then provide the necessary means to fulfill them. The significant changes in the banking system and services, the competitive and rapidly changing environment in which banks operate, and the recognition that consistently delivering superior quality services compared to competitors can create a stronger competitive advantage have driven banks to shift their focus towards customer satisfaction and enhancing service quality. Consequently, due to the characteristics of digital environments, banks are compelled to transition into digital banks and bridge the gap between the physical and digital realms in a timely manner. The journey of banks towards customer-centricity should commence with the adoption of digital technologies and by establishing targeted partnerships and re-engineering the current processes and structure of banks within the digital banking ecosystem. The primary objective of the current research is to conduct a bibliometric analysis of the scientific publications in the Scopus scientific database on the subject of digital banking from its inception to date and to present the global scientific landscape (over the past 66 years). In scientometric research, it is crucial to emphasize the significance of quantitative and qualitative information about the topic being studied. Such studies unveil hidden realities in every scientific domain. Undoubtedly, remote research points may pose significant challenges in the future or reveal remarkable prospects for further research and narrowing research gaps. Methodology: This research follows an analytical-applied approach, utilizing scientometric techniques such as synonym analysis and social network analysis. By examining the main results, the research field's topics and boundaries were meticulously gathered through records and citation links. Under a scientific framework, 3488 sources related to digital banking were extracted from the Scopus scientific database by searching the article title, abstract, and keywords. Data review and analysis were conducted using the database's tools, as well as Excel and VOSviewer software, a social network analysis tool. This was done to quantify, design, and visualize the research products' trends, knowledge map, colleagues' network, and the synonymy network of research on digital banking. Findings: The findings of the research indicate the upward trend of scientific productions in the field of digital banking, especially since the year 2000, and it also indicates that; More than 97% of the indexed documents are in English and the rest are related to other languages of the world and have been retrieved and categorized in the form of 13 types of texts, the highest frequency of which is related to original research articles with 44.70% and countries such as the United States of America, England and India has the most published documents in this field compared to other countries in the world. Conclusion: The findings of the research indicate an upward trend in scientific productions in the field of digital banking, especially since the year 2000. More than 97% of the indexed documents are in English, while the remaining are in various languages from around the world. These documents have been retrieved and categorized into 13 types of texts, with the highest frequency being original research articles at 44.70%. Countries such as the United States of America, England, and India have the most published documents in this field compared to other countries worldwide. Conclusion: Investigations like this can provide a suitable path and roadmap for future research, investment, and policymaking by executive departments in the field of digital banking. The results of this research show that Iranian researchers have also entered this field and have scientific productions and collaborations with other countries in this area, but this issue should be further developed. Additionally, the co-occurrence results of the words show the frequency of terms such as "banking", "digital storage", "fintech", "digital technology", "blockchain", "e-commerce" and "authentication" and the most relevance to the term "digital banking. The results of co-authored works by countries reveal that India, America, England, the Russian Federation, Indonesia, Malaysia, Australia, and Spain have the most significant scientific cooperation in this field compared to other countries. [ABSTRACT FROM AUTHOR]

This study explores the dynamics of emerging technology innovation ecosystems, viewing them as complex systems comprising social actors and knowledge artifacts engaged in innovation interactions. Employing a multilayer network perspective, we present a Social-Knowledge-Science-Technology (A-K-S-T) framework, examining both homogeneous and heterogeneous interactions among innovators and knowledge elements. Within this framework, we map out the technological landscape, identify ecological niches for specific actors and knowledge elements, and gauge knowledge proximity among innovators, revealing opportunities for collaboration and knowledge innovation. Using 5G technology as an illustrative example, key findings include the potential for innovation development in 5G, the need for enhanced collaboration among organizations in related technological fields, and the complementary nature of scientific and technological knowledge. This research contributes to innovation ecosystem literature, offering insights for management, governance, efficiency, and shared prosperity; meanwhile, it is a valuable reference for decision-makers to shape effective strategies. [ABSTRACT FROM AUTHOR]

Embedding collaboration networks in the context of open innovation can facilitate firm innovation. Previous studies have not considered the impact of multilevel network structural embedding on firm innovation. In this study, organizational collaboration networks, knowledge networks, and urban collaboration networks are viewed as systems to explore their impact on innovation quantity and innovation quality. We validate the research hypotheses using data from Chinese high-tech firms in the field of artificial intelligence and intelligent manufacturing equipment. The results indicate that structural holes occupied by firms in organizational collaboration networks can increase the innovation quantity and have a U-shaped effect on innovation quality. Knowledge network structural holes and urban collaboration network structural holes moderate the relationship between organizational collaboration network structural holes and innovation quantity and quality. Our findings will help firms to efficiently utilize the advantages of multilevel network structural holes to improve the innovation quantity and innovation quality. [ABSTRACT FROM AUTHOR]

Knowledge networks in regional clusters are fundamental to support innovation and local development. Within clusters, family firms are key in creating business opportunities and supporting the establishment of inter-organizational networks. Yet, their role within regional clusters for knowledge transfers is still not well understood, especially in comparison with non-family firms. This paper applies Exponential Random Graph Models (ERGMs) to network data collected from the Parabiago cluster, one of the most important Italian footwear clusters, to contribute to a better understanding of the network strategies of family firms. We identify distinct network strategies associated with the cluster firms, accounting for different knowledge exchange types: technological, market, and managerial. In our modelling, we control for firm-level attributes and dyadic-level attributes, such as geographical distance and cognitive proximity between cluster firms. Our results suggest that the proneness of family firms to grow networks is highly robust relative to non-family firm relationships, irrespective of knowledge types being exchanged. Moreover, family firms tend to establish connections with other family firms, showing the presence of homophily in their networking approach; however, non-family firms are rather different, since they do not have the same homophilous approach when it comes to exchange knowledge with other non-family firms. These results indicate that the nature of ownership is driving knowledge exchange differences. This key feature of family-only relationships in clusters may help managers and policymakers in devising more effective and targeted cluster strategies. Plain English Summary: Family firms are key in supporting local development, especially in regional clusters. However, while it is well established that their strategies differ from other (non-family) firms, it is still unclear what is their networking behaviour for supporting knowledge exchange—and thus innovation. This paper provides an empirical overview of this phenomenon, by analyzing an Italian case study: the Parabiago footwear cluster. The results show that (a) family firms are more proactive in establishing network relationships; (b) family firms tend to exchange knowledge with other family firms, while non-family firms do not show the same homophilous approach. Overall, this indicates that policies for clusters need to balance support for distinct business types and recognize the familiness characteristics of regional productive structures. [ABSTRACT FROM AUTHOR]

China has the largest industrial scale and the most abundant industry type among the world.However, due to the influence of many factors, it is necessary to discover the blocking and breakpoints of the industrial chain, identify the stuck points, find alternative channels, and comprehensively optimize the industry.Then, this article explained the construction process of the industry knowledge graph from three aspects: data base construction, core knowledge graph mining, and compatibility with traditional industry chain knowledge.Finally the application scenarios and advantages of industry knowledge graph was analyzed, and an application case was given in the integrated circuit industry.

To what degree has the vast space of higher-order knowledge combinations been explored and how has it evolved over time? To address these questions, we first develop a systematic approach to measuring combinatorial innovation in the biomedical sciences based upon the comprehensive ontology of Medical Subject Headings (MeSH) developed and maintained by the US National Library of Medicine. As such, this approach leverages an expert-defined knowledge ontology that features both breadth (27,875 MeSH analyzed across 25 million articles indexed by PubMed that were published from 1902 onwards) and depth (we differentiate between Major and Minor MeSH terms to identify differences in the knowledge network representation constructed from primary research topics only). With this level of uniform resolution, we differentiate between three different modes of innovation contributing to the combinatorial knowledge network: (i) conceptual innovation associated with the emergence of new concepts and entities (measured as the entry of new MeSH) and (ii) recombinant innovation, associated with the emergence of new combinations, which itself consists of two types: peripheral (i.e. combinations involving new knowledge) and core (combinations comprised of pre-existing knowledge only). Another relevant question we seek to address is whether examining triplet and quartet combinations, in addition to the more traditional dyadic or pairwise combinations, provides evidence of any new phenomena associated with higher-order combinations. Analysis of the size, growth, and coverage of combinatorial innovation yield results that are largely independent of the combination order, with some subtle caveats identified at higher order (i.e. beyond the common dyadic or pairwise representation of combinations). Our main results are two-fold: (a) despite the persistent addition of new MeSH terms, the network is densifying over time meaning that scholars are increasingly exploringand realizing the vast space of all knowledge combinations and (b) conceptual innovation is increasingly concentrated within single research articles, a harbinger of the recent paradigm shift towards convergence science.

China has the largest industrial scale and the most abundant industry type among the world. However, due to the influence of many factors, it is necessary to discover the blocking and breakpoints of the industrial chain, identify the stuck points, find alternative channels, and comprehensively optimize the industry. Then, this article explained the construction process of the industry knowledge graph from three aspects: data base construction, core knowledge graph mining, and compatibility with traditional industry chain knowledge. Finally the application scenarios and advantages of industry knowledge graph was analyzed, and an application case was given in the integrated circuit industry. [ABSTRACT FROM AUTHOR]

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Knowledge sharing is a competitive advantage and necessity for the success of any organization. Meanwhile, knowledge networks have been introduced as a way to enhance knowledge sharing between individuals and as an effective tool to facilitate knowledge exchange in clinical, educational, and commercial fields. The purpose of this paper is to identify the factors that can affect the level of knowledge sharing and exchange between academic and scientific specialists in knowledge networks and Communities of Practice (COP). A systematic literature review was conducted using the PRISMA guidelines. Four databases were searched, including Scopus, Web of Science, PubMed, and ProQuest. Google Scholar search was conducted to complete the search and ensure the tracking of the gray literature. Also, relevant sources, references, and reference lists of the related articles were reviewed. The studies were searched from April until August 2022 and finally the content analysis of the findings was done. Two reviewers independently assessed the quality of included studies. Data were extracted using the Joanna Briggs Institute (JBI) checklist tool. Of the 1439 records, 13 studies met the inclusion criteria. This study identified three main categories of factors affecting knowledge sharing in knowledge networks and COPs as individual factors, organizational, and structural. The results showed that knowledge networks provide opportunities to overcome professional barriers and complex systemic challenges and lead to knowledge sharing and exchange among scientific specialists. This article has important implications for managers, health policymakers, and academics who wish to expand knowledge sharing of scientific specialists through knowledge networks and CoPs in knowledge-based organizations.

A linked data approach provides new opportunities for annotating, interlinking, sharing and enriching massive open online educational resources. However, it can be difficult for non-expert users to build and utilize the educational linked data in educational settings. Thus, flexible and user-friendly ways to represent, interlink, visualize and utilize the educational linked data become increasingly important. This paper proposes a linked data-based knowledge navigation system (LDKNS) for improving the teaching effectiveness. In the system, learning resource ontology was implemented to represent learning contents, and linked data visualization technologies were used in a formal curriculum structure. It presents a use case of building the educational linked data and creating interactive knowledge visualizations to support teachers and students in art design education. A contrast experiment was conducted to evaluate the effectiveness of the proposed system. The experiment involved 115 second year undergraduate students divided into experimental and control groups. It was found that there was a significant increase in the motivation of students who used the LDKNS. It was also found that students' achievement in the experimental group performs better. Furthermore, the results of a survey on cognitive load revealed that using the system can decrease their cognitive load. Thus, we believe that art design education supplemented with the LDKNS yields a significant learning advantage for students by improving learning performance. [ABSTRACT FROM AUTHOR]

Knowledge network construction (KNC) is the essence of dynamic knowledge architecture, and is helpful to illustrate ubiquitous knowledge service in digital libraries (DLs). The authors explore its theoretical foundations and basic rules to elucidate the basic principles of KNC in DLs. The results indicate that world general connection, small-world phenomenon, relevance theory, unity and continuity of science development have been the production tool, architecture aim and scientific foundation of KNC in DLs. By analyzing both the characteristics of KNC based on different types of knowledge linking and the relationships between different forms of knowledge and the appropriate ways of knowledge linking, the basic principle of KNC is summarized as follows: let each kind of knowledge linking form each shows its ability, each kind of knowledge manifestation each answer the purpose intended in practice, and then subjective knowledge network and objective knowledge network are organically combined. This will lay a solid theoretical foundation and provide an action guide for DLs to construct knowledge networks. [ABSTRACT FROM AUTHOR]

Constructing a personalized learning path is a critical way to solve the problem of cognitive difference and learning disorientation effectively. The construction process of the learning path is closely related to the internal relationship between knowledge and needs to meet different learning scenarios and learning needs. Because of the above requirements, a personalized learning path model based on a knowledge network is proposed in this paper. The algorithm begins by building a knowledge network with learning resource nodes and knowledge points. Following that, the order of the knowledge points was determined using their sequential link. A sequence of learning materials that adhere to user characteristics was eventually acquired by evaluating the learning time limit of various learning contexts. The proposed approach was tested on the data sets of open MOOPer and Python learning platforms. Compared with traditional learning path construction algorithms, the proposed algorithm improves the accuracy and relevance. [ABSTRACT FROM AUTHOR]

Fuel cells, as energy conversion devices, are receiving attention from researchers and developers due to their high efficiency and environmentally friendly characteristics. However, despite the prospect of booming fuel cell development, the overall quality and level of development still need to be improved, and safety is one of the key development indicators. This article presents a scientometric and knowledge network analysis of fuel cell safety based on information from 890 relevant publications in the Web of Science Core Collection. The bibliometric software VOSviewer and Co-Occurrence are used for in-depth analysis and visual presentation of the publication information. The results show that the first publication is released in 1991, and this research field has exploded since 2017. Moreover, the USA and China have the strongest cooperation in this field. The hot areas of fuel cell safety research are focused on five clusters: electrode and catalyst safety, fuel cell electric vehicle safety, hydrogen safety, energy generation and storage safety, and solid oxide fuel cell safety. This article maps the knowledge of research related to fuel cell safety and helps readers gain a quick perspective on the research structure and future directions in this field. • Pioneering bibliometric study on safety of fuel cells. • Evaluation of the research trends of publications. • Identification of research themes obtained by keywords. [ABSTRACT FROM AUTHOR]

A Knowledge network can well describe the knowledge development progress during teaching process and the relations of teaching resources. Managing the virtual simulation teaching resources based on knowledge network technology and arranging the scattered resources around each teaching point are very important for teachers to form a complete teaching system. However, there are many problems pending for solutions in research fields such as the construction of Chinese knowledge network and the text management based on knowledge network technology. For this reason, this paper aims to study the management and sharing of building construction virtual simulation teaching resources based on knowledge network analysis. At first, this paper elaborated on the construction process of the knowledge network of teaching resources, and introduced in detail a few steps during the construction process, including the entity/attribute extraction, coreference resolution, and relation extraction, etc. Then, this paper proposed an improved spectral clustering algorithm for the management and sharing of the said teaching resources, and realized the resource management and sharing works on a platform. At last, experimental results verified the validity of the proposed algorithm and the constructed model. [ABSTRACT FROM AUTHOR]

Embedding collaboration networks in the context of open innovation can facilitate firm innovation. Previous studies have not considered the impact of multilevel network structural embedding on firm innovation. In this study, organizational collaboration networks, knowledge networks, and urban collaboration networks are viewed as systems to explore their impact on innovation quantity and innovation quality. We validate the research hypotheses using data from Chinese high-tech firms in the field of artificial intelligence and intelligent manufacturing equipment. The results indicate that structural holes occupied by firms in organizational collaboration networks can increase the innovation quantity and have a U-shaped effect on innovation quality. Knowledge network structural holes and urban collaboration network structural holes moderate the relationship between organizational collaboration network structural holes and innovation quantity and quality. Our findings will help firms to efficiently utilize the advantages of multilevel network structural holes to improve the innovation quantity and innovation quality.

Collaborations between universities and firms provide a key pathway for innovation. In the big data era, however, the interactions between these two communities are being reshaped by information of much higher complexity and knowledge exchanges with more volume and pace. With this research, we put forward a methodology for comprehensively measuring both actor collaboration and produced knowledge in shaping network dynamics of university-industry collaboration. Using dual-layer networks consisting of organizations and topics, we mapped the longitudinal correlations between partnerships and knowledge in terms of both co-applications of patents and semantics. Network structures, individual characteristics, and knowledge proximity indicators were used to depict the longitudinal networks and then model the network dynamics. Further, a stochastic actor-oriented model was used to provide insights into the factors contributing to the network's evolution. A case study on university-industry collaborations in the information and communications technology sector demonstrates the feasibility of the methodology. The result of this study can be used for future research into the mechanisms that underpin university-industry collaborations and opportunity discovery. [ABSTRACT FROM AUTHOR]

This paper incorporates systems thinking in the design of a knowledge networked systems model (KNSM) for supporting knowledge‐to‐action in conservation organizations. It assumes that organizations are complex entities with properties that should be considered normatively. The synthesis of these properties into a KNSM revolves around the most common question many conservation practitioners are asked by the public: how is X doing? X typically reflects a concept easily grasped by the public such as a species or ecosystem. To operationalize a KNSM, one should consider that (1) information, not authority, empowers the network, (2) structural or bureaucratic impediments to maximizing utility of knowledge are minimized, (3) interacting agents direct the flow of information and knowledge and guide agency outputs, (4) boundaries between offices, programs, departments, and organizations are treated as porous, (5) transparency is fundamental to successful model operation, (6), most conservation problems are spatial, and (7) ecological and knowledge hierarchies organize the network. Benefits of this approach include facilitating research outcomes that support management decisions, a tool for strategic planning and implementation, greater transparency and operational clarity, foundation for team building, means for identifying knowledge gaps, a platform for organizational in‐reach and public outreach, facilitated communication in general, and an important tool for collaborative conservation decision making. An example using a hypothetical listed species is presented to illustrate how a KNSM can be designed and implemented. [ABSTRACT FROM AUTHOR]