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This research study aims to explore the factors that influence continuance intention in Blended learning (BL) among business school students in China. By employing a grounded theory approach and utilizing the Fuzzy Set Qualitative Comparative Analysis (FsQCA) method, the research investigates the complex interplay of various variables that contribute to business school students’ intention to continue participating in BL programs. The study includes a comprehensive analysis of factors such as self-efficacy, perceived usefulness, task-technology fit, social influence, and satisfaction. The findings provide valuable insights into the factors that drive business school students’ continuance intention in BL, specifically within the business school context in China. Moreover, the study highlights the non-linear relationships and various configurations of these factors that result in high levels of continuance intention. The results contribute to the existing knowledge base and inform educational institutions in designing effective strategies to enhance business school students’ engagement and promote the long-term success of BL initiatives. [ABSTRACT FROM AUTHOR]

The spatial concentration of knowledge production leads to increased regional inequality, but technology flows have the potential to improve the distribution of innovation. This study examines the role of technology flows in regional specialization at the technology level in China during 2005–2016 using patent data. To unpack technology flows, we distinguish three directions based on patent transactions: trickle-down, proximity and siphon. Results show that regions are more likely to specialize in technological activities, which exhibit a greater number of external linkages characterized by relatively low relatedness and a limited number of strong links. Access to external technological linkages is identified as a key pathway for less innovative regions to achieve place breakthroughs. The technology flows of trickle-down help less innovative regions specialize in more complex technologies than their local knowledge base, while siphon does not significantly impact place breakthroughs in innovative regions. [ABSTRACT FROM AUTHOR]

R&D investment leap is the behavior of an organization to significantly increase or decrease R&D investment in a specific period. Existing literature on R&D investment leap does not pay enough attention to the antecedents. This paper analyzes and tests the knowledge base antecedents of R&D investment leap based on the Punctuated Equilibrium payoff and risk perspective, using a panel data of Chinese listed companies from 2009-2018 as a sample. The results show that technical knowledge base has an opposite effect on R&D investment leap in different directions. Technical diversity and technical complexity have significant positive effects on upward R&D investment leap from exploitation to exploration, technical diversity has significant negative effects on downward R&D investment leap from exploration to exploitation, but technical complexity does not have significant negative effects on downward R&D investment leap. Moreover, environment shock moderate their relationship. When environment shock is advantage, organizations with stronger technical knowledge base are more likely to carry out upward R&D investment; otherwise, organizations with stronger technical knowledge base will slow down the downward R&D investment leap. The results help to enrich and deepen the theoretical study of R&D investment volatility, and have implications for R&D investment decisions in the VUCA environment. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to investigate the antecedents of recombinant innovation from the perspective of ego–network dynamics, and further disentangle whether ego–network stability or ego–network expansion is more conducive to recombinant innovation under heterogeneous knowledge base. Design/methodology/approach: This paper uses 1,801 patent data in China's biotechnology field as a sample and adopts fixed effects regression model to examine the effects of ego–network dynamics on recombinant innovation and further uses the Wald tests to discern which ego–network dynamic is more conducive to recombinant innovation under heterogeneous knowledge base. Findings: The empirical results indicate that ego–network dynamics have a positive impact on recombinant innovation. Specifically, for firms with high knowledge breadth and high knowledge depth as well as high knowledge breadth and low knowledge depth, ego–network stability is more conducive to recombinant innovation. By contrast, for firms with low knowledge breadth and high knowledge depth, recombinant innovation benefits more from ego–network expansion. As for firms with low knowledge breadth and low knowledge depth, both ego–network stability and ego–network expansion can promote recombinant innovation, while the effects are not significant. Practical implications: This research may enlighten managers to choose suitable ego–network dynamics strategies for recombinant innovation based on their knowledge base. Originality/value: This research not only contributes to the literature on recombinant innovation by revealing the impact of different ego–network dynamics on recombinant innovation but also contributes to network dynamics theory by exploring whether ego–network stability or ego–network expansion is more conducive to recombinant innovation under a heterogeneous knowledge base. [ABSTRACT FROM AUTHOR]

The strand research of regional branching identifies relatedness as a key driver of new specializations in a region. The purpose of the present paper is to extend the regional branching framework by investigating how a regional knowledge base of information and communication technologies (ICTs) and others (non-ICTs) influences the specialization of financial technology (FinTech) in China at the city level. Accordingly, the empirical analysis focuses on the relationship between relatedness and FinTech diversification using panel data spanning the period 2008–2021 covering 247 China's cities. We obtain that the role of ICTs relatedness is positively relevant in explaining the emergence and development of new FinTech domains. However, non-ICT relatedness is a negative factor in the process of FinTech diversification, which we attribute to the fact that FinTech is less reliant on other knowledge base except ICT sectors. Additionally, the effects of ICTs relatedness are higher in peripheral cities compared to core cities in promoting FinTech diversification. [ABSTRACT FROM AUTHOR]

This study examines the relationship between CEO career variety, digital knowledge base extension, and digital transformation in a digital M&A context. An empirical test was conducted using regression analysis with the digital M&A events of the new generation of information technology firms in China as the research sample. The results reveal that CEO career variety has a positive effect on digital transformation in the digital M&A context and that digital knowledge-base extension plays a mediating role. Moreover, the heterogeneity impact analysis indicated that the moderating effects of geographical distance, knowledge disparity, and cultural difference between target and acquirer firms on the above relationships vary greatly: geographical distance has a negative moderating effect, cultural difference has a positive moderating effect, and the moderating effects of both geographical distance and cultural difference are realized through mediating effects, but none of the moderating effects of knowledge disparity are significant. [ABSTRACT FROM AUTHOR]

Whereas modern medicine has enabled humans to live longer and more robust lives, recent years have seen a significant increase in chronic care costs. The prevention of threats to mobility is critical for chronic disease management. Researchers and physicians often analyze data from wearable motion sensor–based information systems (IS) to prevent falls. However, prior studies on fall prevention often achieve suboptimal performance because of their limited capacities in modeling data distributions. In this study, we adopt the computational design science paradigm to develop a novel fall prevention framework, which includes the hidden Markov model with generative adversarial network (HMM-GAN) that extracts temporal and sequential patterns from sensor signals and recognizes snippet states and a logistic regression that utilizes the snippet states and determines whether and when to trigger protective devices to prevent fall injuries. We evaluate the proposed framework against prevailing fall-prevention models and the HMM-GAN component against state-of-the-art sensor analytics models on large-scale data sets. Through an in-depth case study, we demonstrate how the proposed framework can lead to significantly reduced potentially catastrophic falls. Besides practical health information technology contributions, HMM-GAN offers methodological contributions to the IS knowledge base for scholars designing novel IT artifacts for healthcare applications. Whereas modern medicine has enabled humans to live longer and more robust lives, recent years have seen a significant increase in chronic care costs. The prevention of threats to mobility, such as falls, freezing of gait, and others, is critical for chronic disease management. Researchers and physicians often analyze data from wearable motion sensor–based information systems (IS) to prevent falls because of their convenience, low cost, and user privacy protection. However, prior studies on fall prevention often achieve suboptimal performance because of their limited capacities in modeling data distributions. In this study, we adopt the computational design science paradigm to develop a novel fall prevention framework, which includes the hidden Markov model with generative adversarial network (HMM-GAN) that extracts temporal and sequential patterns from sensor signals and recognizes snippet states, and a logistic regression that utilizes the snippet states and determines whether and when to trigger protective devices to prevent fall injuries. Drawing upon the HMM, deep learning, and a new expectation-maximization instantiation, the proposed framework addresses limitations of existing methods by automatically extracting features from motion sensor data, accounting for both independent and sequential information in data snippets, operating on sensor signals with varying distributions and sharp peaks and valleys, allowing lead times, and being applicable in both semisupervised and supervised modes. We evaluate the proposed fall prevention framework against prevailing fall prevention models and the HMM-GAN component against state-of-the-art sensor analytics models on selected large-scale ground truth data sets containing thousands of falls and normal activities. Through an in-depth case study, we demonstrate how the proposed framework can lead to significantly reduced potentially catastrophic falls by senior citizens and produce more than $33 million of economic benefits over competing models. Besides practical health information technology contributions, HMM-GAN offers methodological contributions to the IS knowledge base for scholars designing novel information technology artifacts for healthcare applications. History: Olivia Sheng, Senior Editor; Huimin Zhao, Associate Editor. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72293581, 91846201, 72293580, 72188101, 72101079, 71771131, and 72110107003], the Division of Industrial Innovation and Partnerships [Grant 1622788], and the Division of Computer and Network Systems [Grant 1850362] of the National Science Foundation. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2023.1203. [ABSTRACT FROM AUTHOR]

The negative public opinions and views on overseas direct investment (ODI) of a multinational enterprise (MNE) will damage the image of its brand and are likely to bring it serious economic and social losses. So, it is important for the MNE to understand the formation and spread mechanism of public opinion risk (POR) in order to effectively respond to and guide the public opinion. This research proposed a multifractal-based situation optimization method to explore the POR evolution based on the media-based negative sentiment on China's ODI. The sentiment measurement is obtained by a directed crawler for gathering the text of media reports corresponding to a certain ODI event using a URL knowledge base from the GDELT Event Database. Taking the public opinion crisis of the tax evasion incident of the local arm of China's MNE in India as an example, the experiments show that this method could dynamically monitor the POR event in real-time and help MNE guide the effective control and benign evolution of public opinion of the event. [ABSTRACT FROM AUTHOR]

The article uses a longitudinal case study of China's high-speed rail from the sectoral system perspective to show how a latecomer achieves catch up in the context of complex products and systems (CoPS). Results indicate the coevolution among high-speed train CoPS, the complementary assets, and government engagement are important to the sectoral system's catch up. To achieve catch up, a latecomer can choose a strategy in which the complementary assets take precedence over the CoPS, and the coordination of the two systems is maintained with government engagement. In addition, two elements involving the sectoral system's knowledge base and government engagement play fundamental roles enabling the evolution of the competences of the CoPS and the leverage of complementary assets for catch up. The article contributes to the literature on catch up in the CoPS context using a sectoral system framework. [ABSTRACT FROM AUTHOR]

Indoor fire is a sudden and frequent disaster that severely threatens the safety of indoor people worldwide. Indoor fire emergency evacuation is crucial to reducing losses involving various objects and complex relations. However, traditional studies only rely on numerical simulation, which cannot provide adequate support for decision-making in indoor fire scenarios. The knowledge graph is a knowledge base that can fully utilize massive heterogeneous data to form a sound knowledge system; however, it has not been effectively applied in the fire emergency domain. This study is a preliminary attempt to construct a knowledge graph for indoor fire emergency evacuation. We constructed the indoor fire domain ontology and proposed a four-tuple knowledge representation model. A knowledge graph was constructed with 1852 nodes and 2364 relations from 25 indoor fire events. The proposed method was tested for the case study of Henan Pingdingshan '5.25' Fire Accident in China. Results show that the proposed knowledge representation model and the corresponding knowledge graph can represent complicated indoor fire events and support indoor fire emergency evacuation. [ABSTRACT FROM AUTHOR]

Integrated traditional Chinese medicine (TCM) and Western medicine (WM) is a new medical science grounded in the knowledge bases of both TCM and WM, which then forms a unique modern medical system in China. Integrated TCM and WM has a long history in China, and has made important achievements in the process of clinical diagnosis and treatment. However, the methodological defects in currently published clinical practice guidelines limit its development. The organic integration of TCM and WM is a deeper integration of TCM and WM. To realize the progression of "integration" to "organic integration", a targeted and standardized guideline development methodology is needed. Therefore, the purpose of this study is to establish a standardized development procedure for clinical practice guidelines for the organic integration of TCM and WM to promote the systematic integration of TCM and WM research results into clinical practice guidelines in order to achieve optimal results as the whole is greater than the sum of the parts. [ABSTRACT FROM AUTHOR]

Cultural heritage management poses significant challenges for museums due to fragmented data, limited intelligent frameworks, and insufficient applications. In response, a digital cultural heritage management approach based on knowledge graphs and deep learning algorithms is proposed to address the above challenges. A joint entity-relation triple extraction model is proposed to automatically identify entities and relations from fragmented data for knowledge graph construction. Additionally, a knowledge completion model is presented to predict missing information and improve knowledge graph completeness. Comparative simulations have been conducted to demonstrate the effectiveness and accuracy of the proposed approach for both the knowledge extraction model and the knowledge completion model. The efficacy of the knowledge graph application is corroborated through a case study utilizing ceramic data from the Palace Museum in China. This method may benefit users since it provides automated, interconnected, visually appealing, and easily accessible information about cultural heritage. [ABSTRACT FROM AUTHOR]

Urban renewal is one of the most important topics in urban studies. However, this topic still does not receive a systematic review by bibliometrics to reveal the current situation. Therefore, the study collected data from the Web of Science core collection and applied statistical and co-occurrence analysis to recognize the productive academic entities of crucial journals and publications and the relationship between references, journals, and keywords. Results show that China receives the most attention currently, while the United States began related research as a pioneer, and the international scholarly collaboration in urban renewal is uneven. However, global academia gradually focuses on sustainability and energy utilization. Furthermore, there are four hotspots: the practices and theories in urban renewal, environment protection, future energy utilization, and the urban environment's impact on individuals. The study can assist related stakeholders in recognizing the foci, research trends, and knowledge base to understand urban renewal better. [ABSTRACT FROM AUTHOR]

Purpose: The outbreak of COVID-19 pandemic has posed severe challenges to infrastructure construction in China. Particularly, the complex technology and high process uncertainty of deep foundation pit construction make its safety risk identification a challenging issue of general concern. To address these challenges, Building Information Modeling (BIM) can be used as an important tool to enhance communication and decision-making among stakeholders during the pandemic. The purpose of this study is to propose a knowledge management and BIM-integrated safety risk identification method for deep foundation pit construction to improve the management efficiency of project participants. Design/methodology/approach: This paper proposes a risk identification method that integrates BIM and knowledge management for deep foundation pit construction. In the framework of knowledge management, the topological relationships between objects in BIM are extracted and visualized in the form of knowledge mapping. After that, formal expressions of codes are established to realize the structured processing of specification provisions and special construction requirements. A comprehensive plug-in for deep foundation pit construction is designed based on the BIM software. Findings: The proposed method was verified by taking a sub-project in deep foundation pit project construction as an example. The result showed the new method can make full use of the existing specification and special engineering requirements knowledge. In addition, the developed visual BIM plug-in proves the feasibility and applicability of the proposed method, which can help to increase the risk identification efficiency and refinement. Originality/value: The deep foundation pit safety risk identification is challenged by the confusion of deep foundation pit construction safety knowledge and the complexity of the BIM model. By establishing the standardized expression of normative knowledge and special construction requirements, the efficiency and refinement of risk identification are improved while ensuring the comprehensiveness of results. Moreover, the topology-based risk identification method focuses on the project objects and their relations in the way of network, eliminating the problem of low efficiency from the direct BIM-based risk identification method due to massive data. [ABSTRACT FROM AUTHOR]

Introduction: Sclerotium rolfsii Sacc. is a globally dispersed pathogenic fungus that causes southern blight disease in many crops and Chinese herbal medicine. The high degree of variation and diversity in the fungi altered population genetic structure. Therefore, the important factors of variation within the pathogen population should be considered during the development of management strategies for the disease. Methods: In this study, S. rolfsii isolates from 13 hosts in 7 provinces of China were collected and analyzed to identify their morphological features and perform molecular characterization. To develop EST-SSR primers, transcriptome sequencing was performed on isolated CB1, and its SSR loci were comprehensively analyzed. In addition, we analyzed the polymorphisms among different populations based on screened EST-SSR primers. Results: The results showed that all of these clean reads with total 36,165,475 assembled bases were clustered into 28,158 unigenes, ranged from 201 bp to 16,402 bp on the length, of which the average length was 1,284 bp. Of these, the SSR sequence appeared at an average interval of 15.43 kB, and the frequency of SSR was 0.0648 SSR/kB. Polymorphism of 9 primers was observed among 22 populations, and was verified by the Shannon’s index (average = 1.414) and polymorphic information index (> 0.50). The genetic diversity analysis revealed diversity in all host populations and geographical populations. Further, molecular variance analysis (AMOVA) showed that the differences between groups were mainly related to geographical location. Based on cluster analysis, the 7 populations were roughly divided into 3 groups, and the results were highly consistent with those based on the geographical location, ultimately aligning with the results of STRUCTURE analysis. Discussion: The findings build on current knowledge of the distribution of S. rolfsii in the southwest area of China, adding value to current knowledge base on the population structure and genetic diversity of S. rolfsii, specifically in the context of Chinese herbal medicine cultivation in China. Overall, our findings may provide valuable information for breeding of crops with enhanced resistance toward S. rolfsii. [ABSTRACT FROM AUTHOR]

Purpose: Epistaxis is a common symptom and can be caused by various diseases, including nasal diseases, systemic diseases, etc. Many misdiagnosis and missed diagnosis of epistaxis are caused by lack of clinical knowledge and experience, especially some interns and the clinicans in primary hospitals. To help inexperienced clinicans improve their diagnostic accuracies of epistaxis, a computer-aided diagnostic system based on Dynamic Uncertain Causality Graph (DUCG) was designed in this study. Methods: We build a visual epistaxis knowledge base based on medical experts' knowledge and experience. The knowledge base intuitively expresses the causal relationship among diseases, risk factors, symptoms, signs, laboratory checks, and image examinations. The DUCG inference algorithm well addresses the patients' clinical information with the knowledge base to deduce the currently suspected diseases and calculate the probability of each suspected disease. Result: The model can differentially diagnose 24 diseases with epistaxis as the chief complaint. A third-party verification was performed, and the total diagnostic precision was 97.81%. In addition, the DUCG-based diagnostic model was applied in Jiaozhou city and Zhongxian county, China, covering hundreds of primary hospitals and clinics. So far, the clinicians using the model have all agreed with the diagnostic results. The 432 real-world application cases show that this model is good for the differential diagnoses of epistaxis. Conclusion: The results show that the DUCG-based epistaxis diagnosis model has high diagnostic accuracy. It can assist primary clinicians in completing the differential diagnosis of epistaxis and can be accepted by clinicians. [ABSTRACT FROM AUTHOR]

R&D internationalization is an important way for enterprises in emerging economies to achieve technology catch-up and maintain sustainable development. The direct relationship between R&D internationalization and innovation performance has been widely explored, but the research on the influence of the mechanism between these two elements above is relatively less. This paper takes China's knowledge-intensive listed enterprises from 2009 to 2015 as the research object and uses Poisson fixed effect regression, hierarchical regression, and bootstrap methods to study the mechanism of knowledge base between enterprise R&D internationalization and innovation performance and the moderating effects of institutional distance on the mechanism effect. The results show that: (1) Enterprises' innovation performance can be promoted with the increase of R&D internationalization degree and diversity. (2) Knowledge Base (scope and entropy) plays a mechanism function between R&D internationalization and innovation performance. (3) By negatively moderating the relationship between the knowledge base and innovation performance, the institutional distance between home and host country negatively moderates the knowledge base's mechanism function. [ABSTRACT FROM AUTHOR]

Whether international ventures can benefit from their marketing capability in emerging markets is a critical but underresearched issue. Drawing on the resource-based view (RBV), the authors propose that marketing capability can enhance international ventures' new product performance. However, its effect is contingent on environmental forces (i.e., market uncertainty and technological turbulence) in the highly uncertain and turbulent environments of emerging markets. Using survey data of international ventures in high-tech industries in China, the authors find that marketing capability is less effective for new product development when firms face high levels of market uncertainty or technological turbulence. At that time, enhancing knowledge breadth represents a critical strategic solution to address the decreased efficacy of marketing capability in driving innovation. [ABSTRACT FROM AUTHOR]

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The dislocation between regional innovation and economic development directly influences the economic effect of regional innovation. However, no in-depth researches have been made on how to solve this problem. Using data from Henan Province, China, employing geographical detector technology, this paper focuses on testing whether the industry-university-research cooperation can contribute to coordinating the relation between regional innovation and economic development. It is shown that: 1) the industry-university-research cooperation in Henan Province is increasing gradually, and the network presents a core-edge structure, and the coupling degree between regional innovation and economic development is spatially unbalanced, which is similar to the spatial distribution of the intensity of industry-university-research cooperation; 2) as an important approach to effectively connect scientific researches with market demands, the industry-university-research cooperation can help form an interactive, interconnected, coupled and coordinated virtuous relation between regional innovation and economic development. Compared with the cooperation between organizations of the same type and the separate innovation of organizations, the improvement of the industry-university-research cooperation level can better coordinate the relation between regional innovation and economic development; 3) the cooperative innovation model between enterprises and universities can better promote the coupling between regional innovation and economic development, compared with many industry-university-research cooperation models. For underdeveloped areas lacking local knowledge base, industry-university-research cooperation should be considered as a long-term development strategy, especially using the knowledge sources of external universities and scientific research institutions to enhance innovation capability and achieve economic growth. [ABSTRACT FROM AUTHOR]

Knowledge graph-based question answering is an intelligent approach to deducing the answer to a natural language question from structured knowledge graph information. As one of the mainstream knowledge graph-based question answering approaches, information retrieval-based methods infer the correct answer by constructing and ranking candidate paths, which achieve excellent performance in simple questions but struggle to handle complex questions due to rich entity information and diverse relations. In this paper, we construct a joint system with three subsystems based on the information retrieval methods, where candidate paths can be efficiently generated and ranked, and a new text-matching method is introduced to capture the semantic correlation between questions and candidate paths. Results of the experiment conducted on the China Conference on Knowledge Graph and Semantic Computing 2019 Chinese Knowledge Base Question Answering dataset verify the superiority and efficiency of our approach. [ABSTRACT FROM AUTHOR]

We developed a workflow for the search and screening of natural products by drawing from worldwide experiences shared by online platform users, illustrated how to cope with COVID-19 with a text-mining approach, and statistically tested the natural product identified. We built a knowledge base, which consists of three ontologies pertaining to 7653 narratives. Mustard emerged from texting mining and knowledge engineering as an important candidate relating to COVID-19 outcomes. The findings indicate that, after controlling for the containment index, the net import of mustard is related with reduced total and new deaths of COVID-19 for the non-vaccination time period, with considerable effect size (>0.2). [ABSTRACT FROM AUTHOR]

BACKGROUND: Aphasia is one of the most common complications in patients with ischemic stroke. Studies have shown that acupuncture can improve the symptoms of aphasia patients. However, the effect of acupuncture on language function in patients with ischemic stroke is still controversial. OBJECTIVE: This study aimed to critically assess the efficacy and safety of acupuncture for aphasia following ischemic stroke. METHODS: PubMed, Embase, Cochrane Central Register of Controlled Trials, Web of Science Core Collection, China National Knowledge Infrastructure, Wanfang Digital Periodicals, and Chinese Science and Technology Periodicals database were searched. All randomized controlled trials (RCTs) that met the criteria were included. RESULTS: Meta-analyses showed that mean difference in change of auditory comprehension score (MD = 7.71, 95% CI: 1.83 to 13.59, P = 0.01), spontaneous speech (MD = 2.77, 95% CI: 0.59 to 4.95, P = 0.01), repetition score (MD = 14.48, 95% CI: 11.04 to 17.91, P < 0.00001) and naming score (MD = 14.60, 95% CI: 11.33 to 17.88, P < 0.00001) measured by WAB scale were statistically significant. Subgroup analyses demonstrated that there were statistically significant mean differences in four items of WAB scale in patients with sub-acute stroke, and no statistically significant differences in patients with acute stroke. CONCLUSION: The present study suggests that acupuncture may improve the language function of patients with aphasia following ischemic stroke, especially during the sub-acute phase. However, due to insufficient sample sizes and information on the safety, more high-quality RCTs are still needed [ABSTRACT FROM AUTHOR]

Through a literature analysis, this study proposes that the difference between scientific innovation and technological innovation has been ignored in the current research on the level of scientific and technological innovation and its influencing factors. Combined with multidimensional proximity and knowledge type of current research, a theoretical induction has been carried on their corresponding relation with scientific innovation and technological innovation, research hypotheses were proposed the multidimensional proximity effect on the mode and degree of scientific innovation and technological innovation, five theoretical factors, which are the economic development level, regional economic structure, the level of opening to the outside world, science and technology input and education input, are proposed to affect the level of scientific innovation and technological innovation. In this study, the Yangtze River Delta region of China from 2001 to 2018 is selected as the research sample, and the two hypotheses proposed are tested through a mixed method of exploratory spatial data analysis and spatial panel econometric model. The main conclusions are as follows: i) As an exogenous variable, geographical proximity has a small impact on the level of scientific innovation, but a large impact on the level of technological innovation; ii) As endogenous variables, theoretical influencing factors may not play a significant role in the actual environment due to the complex influence of multidimensional proximity; iii) Based on the idea of improving multidimensional proximity and the actual situation of the region and the city, we can formulate policies conducive to improving the regional and urban innovation environment. [ABSTRACT FROM AUTHOR]

Introduction: Owing to an increasing demand for a continuous and coordinated health service, integrated care is being promoted worldwide. Chinese research on integrated care has rapidly increased over the last 20 years. However, popular topics, paths and trends of integrated care research in China have not been systematically summarised. The study aimed to examine the evolution of integrated care research in China and predict future research trends. Methods: We searched for integrated care research in China published 2000-2020 in Chinese (China National Knowledge Infrastructure) and English (Web of Science). Research articles that met the inclusion criteria were selected. CiteSpace 5.7.R3 was used to perform keyword clustering, timeline view and burst detection analyses. Results: We included 786 Chinese articles and 124 English articles. Chinese articles formed 10 clusters with 1814 keywords. English articles formed 5 clusters with 487 keywords. From 2000 to 2020, integrated care research in China comprised three stages: (1) In the start-up stage (2000-2007), keywords mainly focus on medical resource integration and two-way referral; (2) In the emergence stage (2008-2015), keywords primarily include integrated model, benefits of integration, paths to integration and incentive mechanisms; (3) In the maturation stage (2016-2020), the main keywords are patient preferences, shared management mechanisms, symbiosis theory, valuebased care, payment methods and people-oriented care. Discussion: With increasing health care system reform, popular integrated care research topics in the next stage will likely focus on people-oriented integrated care, service value and payment method reform. Academic research on integrated care in China will help to shape and lead policymaking. Conclusions: Integrated care research in China has gone through three stages over the last two decades. In the future, integrated care theory in China will be informed by concepts from other fields, such as value co-creation in public management, to address the current problem of lack of synthesis in integrated care in China. [ABSTRACT FROM AUTHOR]

Aims: Karst ecological restoration (KER) publication characteristics, topic trends, and knowledge domains were analysed to provide a global perspective. Methods: Bibliometric and knowledge map analyses were used to analyse KER documents (319) in the WoSCC. The whole period was divided into the germination (GP), slow growth (SGP) and rapid growth (RGP) periods based on the number of annual publications (NAP). Results: (1) The NAP increased over time, with China contributing the most. (2) The study topics varied and flourished over time. Plant was the most studied class and Animal the least studied in the Biology category. In the Environment category, Water was the most studied medium in the GP, while Soil was the most studied in the SGP and RGP. Remote sensing and Model were the leading methods in the SGP and RGP; Field investigation and/or experimental methods, including Leaf traits, Vegetation investigation, and Chlorophyll fluorescence, were the main methods in the GP. Ecological engineering was the leading restoration method overall. Planting pattern was a new restoration method in the SGP, while Biochar, Seed bank, and Soil translocation were new methods in the RGP. (3) Eleven knowledge domains and 10 vital documents well support studies on KER. Future research directions are proposed. Conclusions: The results provide a new, global, objective perspective for understanding KER research over the past 3 decades. [ABSTRACT FROM AUTHOR]

Whenever the policy challenges to address ask for a strong scientific background, decision makers depend on the underlying research activities. One of the fields in which research is able to affect the design and implementation of policies is given by the Circular Economy (CE). Our purpose in this paper is to understand how scholars carry out their research in the CE to realize whether a common pattern emerge and identify implications for policy. We provide a systematic investigation of the world's ten largest national research communities (NRCs) to highlight differences and similarities and understand the underlying patterns of research. We refer to eight key dimensions, which are mostly original ones and, therefore, represent elements of novelty of the paper: internationality, interdisciplinarity, specialization, relevance of local issues, policy implications, provision of case studies, research focus and research method. A dataset with 3027 CE-related research publications that range from 2001 to 2018 has been used for the elaborations. Results show some similarities and differences: everywhere, the empirical method prevails over the theoretical, and research is mostly focused on the micro-level of analysis (e.g., consumers and firms), while national research communities differ with respect to the openness to international collaborations, the degree with which some specific disciplines contribute to research in the field of CE, and the consideration of local issues provide. • A cross-country comparison of national research communities (NRCs) studying CE is offered. • NRCs are compared along eight key-dimensions using eleven indicators. • Empirical methods prevail over theory and research focus is on micro-level analyses. • Differences emerge with respect to interdisciplinarity and attention to local issues. • China, US, and European countries are moving along diverse research trajectories. [ABSTRACT FROM AUTHOR]

The KBQA (Knowledge-Based Question Answering) system is an essential part of the smart customer service system. KBQA is a type of QA (Question Answering) system based on KB (Knowledge Base). It aims to automatically answer natural language questions by retrieving structured data stored in the knowledge base. Generally, when a KBQA system receives the user's query, it first needs to recognize topic entities of the query, such as name, location, organization, etc. This process is the NER (Named Entity Recognition). In this paper, we use the Bidirectional Long Short-Term Memory-Conditional Random Field (Bi-LSTM-CRF) model and introduce the SoftLexicon method for a Chinese NER task. At the same time, according to the analysis of the characteristics of application scenario, we propose a fuzzy matching module based on the combination of multiple methods. This module can efficiently modify the error recognition results, which can further improve the performance of entity recognition. We combine the NER model and the fuzzy matching module into an NER system. To explore the availability of the system in some specific fields, such as a power grid field, we utilize the power grid-related original data collected by the Hebei Electric Power Company to improve our system according to the characteristics of data in the power grid field. We innovatively make the dataset and high-frequency word lexicon in the power grid field, which makes our proposed NER system perform better in recognizing entities in the field of power grid. We used the cross-validation method for validation. The experimental results show that the F1-score of the improved NER model on the power grid dataset reaches 92.43%. After processing the recognition results by using the fuzzy matching module, about 99% of the entities in the test set can be correctly recognized. It proves that the proposed NER system can achieve excellent performance in the application scenario of a power grid. The results of this work will also fill the gap in the research of intelligent customer-service-related technologies in the power grid field in China. [ABSTRACT FROM AUTHOR]

Machine learning approaches have exhibited high potential in power system transient stability assessment (TSA), yet their initial preparation stages of stability knowledge base generation (SKBG) based on time-domain simulations often undergo high computational costs. In fact, how to ease the heavy computational burden of SKBG without sacrificing the reliability is still a significant challenge. To address this problem, this paper develops a semi-supervised ensemble learning (SSEL) framework for reliable SKBG acceleration, without additional need for computing hardware upgrading. In particular, it performs detailed simulations for a minority of cases and fast simulations for the majority ones to reduce the total computation time. Considering the absence of stability status information of those fast simulated cases, an SSEL scheme is systematically designed to reliably label their stability status. Before implementing SSEL, two concise feature descriptors are first introduced to efficiently extract transient features from multiplex system trajectories. Then, all the cases are characterized in a unified feature space, whereby a series of semi-supervised support vector machines are trained in randomly formed subspaces. Afterward, these single machines are systematically combined to derive an enhanced SSEL model, which is able to make reliable and robust labeling decisions. Further, a backtrace strategy is carefully devised for SSEL, so as to maintain the high reliability of SKBG. Test results on the IEEE 39-bus system and the realistic GD Power Grid in South China illustrate the excellent performances of the proposed framework on SKBG acceleration. [ABSTRACT FROM AUTHOR]

With the increasing trend of militarization of space and cyberspace, China's space strategy is facing increasingly serious challenges. To secure space cybersecurity, there is an urgent need to develop a situational understanding capability for satellite cyberspace. In this paper, we first propose the key questions that need to be answered for situational understanding, construct inference rules based on satellite network situational understanding ontology and satellite network situational understanding knowledge map, propose a knowledge-based analysis method for satellite cyberspace situational understanding, and realize automatic correlation analysis of multi-source intelligence for satellite cybersecurity by reasoning on satellite cyberspace situational understanding knowledge base. Finally, the correctness and effectiveness of the proposed method are vended by case evaluation, which provides support for China's space-based asset protection auxiliary decision- making. [ABSTRACT FROM AUTHOR]

The location of emerging industries has been hotly debated in the field of economic geography. While the concept of Windows of Locational Opportunity proposes that new industries emerge with relative freedom in space, the literature on Regional Branching has been underlining the importance of existing knowledge base. The concept of related variety provides us with a new way to handle this ongoing debate. By dividing technological knowledge base of a region into related and unrelated knowledge bases, this study empirically examines the impact of knowledge base on technological innovation of emerging industries by utilizing patent data of China's fuel cell industry from 2000 to 2016. The empirical findings clearly confirm the stable and positive impact of related technological knowledge base on the emergence and development of emerging industries. However, the direction of the impact of unrelated technological knowledge base changes from negative to positive with the maturity of the industry. To an increasing extent, emerging industries tend to be less free in space and prefer to locate in developed regions with related and unrelated knowledge. [ABSTRACT FROM AUTHOR]

Purpose: With the rapid development of the economy, carbon emissions have also risen sharply. This study explores the relationship between the two by combining the literature of relevant fields and maps the analytical framework from the knowledge base to the research frontier model using CiteSpace. Design/methodology/approach: Using CiteSpace and data statistical tools, we conducted a bibliometric and visual analysis of nearly ten thousand research papers on carbon emissions and economic development published in the Web of Science (WOS) and China National Knowledge Infrastructure (CNKI) databases from 1991 to 2021. Findings: It shows that research on economic development and carbon emissions is developing steadily and involves a wide range of fields. Notably, keywords such as "carbon emissions," "economic growth," and "energy consumption" had high frequency, centrality, and persistence. "carbon emissions," "economic growth," and "energy consumption" had high frequency, centrality, and persistence. Research institutions in the USA and China have made great contributions to research on economic development and carbon emissions. The authors should continue to enrich and improve research on related subjects and concerns to reasonably plan the path of carbon emission reduction and economic development. Originality/value: The study analyzes the evolution of the relationship between carbon emissions and economic growth to provide scholars a more comprehensive and in-depth understanding of the relationship from an international perspective. [ABSTRACT FROM AUTHOR]

Diptera is often considered to be the richest insect group due to its great species diversity and broad ecological versatility. However, data on dipteran diversity from subtropical ecosystems have hitherto been scarce, due to the lack of studies conducted at an appropriate large scale. We investigated the diversity and composition of Diptera communities on Tianmu Mountain, Zhejiang, China, using DNA metabarcoding technology, and evaluated their dynamic responses to the effects of slope aspect, season, and altitudinal zone. A total of 5,092 operational taxonomic units (OTUs) were discovered and tentatively assigned to 72 dipteran families, including 2 family records new for China and 30 family records new for the locality. Cecidomyiidae, Sciaridae, and Phoridae were the predominant families, representing 53.6% of total OTUs, while 52 families include >95% unidentified and presumed undescribed species. We found that the community structure of Diptera was significantly affected by aspect, seasonality (month) and elevation, with richer diversity harbored in north‐facing than south‐facing slopes, and seasonality a more profound driver of community structure and diversity than elevation. Overall, massive species diversity of Diptera communities was discovered in this subtropical ecosystem of east China. The huge diversity of potentially undescribed species only revealed by metabarcoding now requires more detailed taxonomic study, as a step toward an evolutionary integration that accumulates information on species' geographic ranges, ecological traits, functional roles, and species interactions, and thus places the local communities in the context of the growing knowledge base of global biodiversity and its response to environmental change. [ABSTRACT FROM AUTHOR]

We investigate structural change in the PR China during a period of particularly rapid growth 1998-2014. For this, we utilize sectoral data from the World Input-Output Database and firm-level data from the Chinese Industrial Enterprise Database. Starting with correlation laws known from the literature (Fabricant's laws), we investigate which empirical regularities hold at the sectoral level and show that many of these correlations cannot be recovered at the firm level. For a more detailed analysis, we propose a multi-level framework, which is validated empirically. For this, we perform a robust regression, since various input variables at the firm-level as well as the residuals of exploratory OLS regressions are found to be heavy-tailed. We conclude that Fabricant's laws and other regularities are primarily characteristics of the sectoral level which rely on aspects like infrastructure, technology level, innovation capabilities, and the knowledge base of the relevant labor force. We illustrate our analysis by showing the development of some of the larger sectors in detail and offer some policy implications in the context of development economics, evolutionary economics, and industrial organization. [ABSTRACT FROM AUTHOR]

The debate on the role of knowledge specialization compared to diversification as a driver of regional innovation has been conflicting for a long time. This article contributes to this framework by exploring the effect of knowledge bases of analytical industry represented by China's biotechnology industry on regional innovation from the perspective of networks. The network connecting the industry's patented technology to the cities where the inventors are located depicts the geography of innovation in this industry. The more patents that a city generates in the core technology fields, the greater its centrality in the network, and the greater its relative importance to the industry innovation. Knowledge depth and breadth, two distinct dimensions of the knowledge bases on behalf of specialization and diversification, affect a city's centrality in the technological network. In the analytical industry, both the knowledge depth and breadth of the city have negative curvilinear effects on its centrality in the network. The influence of knowledge depth is greater than that of knowledge breadth, but the synergy between the two is not significant. To promote the innovation performance of analytical industries, peripheral cities are more likely to increase knowledge breadth, while core cities are more likely to increase knowledge depth. [ABSTRACT FROM AUTHOR]

In this study we examined the influence of knowledge network capability on organizational innovation. Drawing on organizational network theory and taking a knowledge-based view, we proposed ambidextrous innovation behavior and network position transition as mediators to clarify the underlying mechanisms of the effect of knowledge network capability on organizational innovation. Furthermore, we proposed knowledge base as a boundary variable to understand its interplay with knowledge network capability in regard to organizational innovation. Participants were 265 senior managers of recently established high-tech enterprises in China. Consistent with our predictions, the results show there was a positive relationship between knowledge network capability and ambidextrous innovation behavior, and network position transition mediated this relationship. In addition, the results suggest that knowledge base can strengthen the direct effect of knowledge network capability on organizational innovation. Our findings enrich the literature on knowledge networks and innovation, and provide important implications for enterprises to innovate by employing knowledge network capability. [ABSTRACT FROM AUTHOR]

Objective: A scientometric analysis to obtain knowledge mapping of Coptis revealed the current research situation, knowledge base and research hotspots in Coptis research. Methods: Coptis -related documents published from 1987 to 2020 were selected through the Web of Science Core Collection. CiteSpace, VOSviewer and Microsoft Excel were used to construct knowledge maps of the Coptis research field. Results: A total of 367 documents and their references were analyzed. These papers were primarily published in mainland China (214), followed by Japan (57) and South Korea (52), and they each formed respective cooperation networks. The document co-citation analysis suggested that the identification of Coptis Salisb. species, the production of alkaloids, and the mechanisms of action of these alkaloids formed the knowledge bases in this field. A keyword analysis further revealed that the research hotspots were primarily concentrated in three fields of research involving berberine, Coptis chinensis Franch, and Coptis japonica (Thunb) Makino. Oxidative stress, rat plasma (for the determination of plasma alkaloid contents), and Alzheimer's disease are recent research hotspots associated with Coptis. Conclusion: Coptis research was mainly distributed in three countries: China, Japan, and South Korea. Researchers were concerned with the identification of Coptis species, the production of Coptis alkaloids, and the efficacy and pharmacological mechanism of the constituent alkaloids. In addition, the anti-oxidative stress, pharmacokinetics, and Alzheimer's disease treatment of Coptis are new hotspots in this field. This study provides a reference for Coptis researchers. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to explore how to build firm's radical innovation capability from its own knowledge resources. It proposes the impact of knowledge base on radical innovation and outlines why and how appropriability regimes are important throughout the radical innovation capability development process. Design/methodology/approach: Questionnaire and in-depth interviews were used to empirically test the relationship between knowledge base, appropriability regimes and radical innovation capability. To correct for potential endogeneity, a two-stage regression model was used to test the interaction effects Findings: By examining 237 firms involved in R&D activities in high-tech manufacturing firms in China, this paper finds that when a firm have a deep knowledge base, strategic appropriability regime may reduce concerns about problems such as knowledge leakage and innovation appropriation associated with deep knowledge bases. In contrast, legal appropriability regime will also tend to strengthen the positive effect of knowledge breadth on radical innovation capability. Research limitations/implications: In this study, self-reported measures are used because of their potential for concept specific accuracy; future research might wish to replicate our model and test it with objective data. Practical implications: The paper includes implications for the fit between the existing internal knowledge base and the way a firm appropriates from its knowledge, and provides entrepreneurs and managers with direct implications about how to manage knowledge resources for radical innovation capability development. Originality/value: This paper provides a more nuanced understanding of how knowledge base and value appropriability mechanisms jointly affect radical innovation capability development. [ABSTRACT FROM AUTHOR]

[Display omitted] • Scientometric analysis of MPs in soil ecosystems was performed. • A total of 2210 articles on MPs from 2012 to 2022 were analyzed. • Knowledge bases, trends, and hotspots in MP research were identified. • China was the top country in terms of publications and funding. • The migration of MPs from terrestrial to aquatic systems should be considered in the future. Soil microplastic (MP) pollution is a growing global environmental issue leading to a series of ecosystem problems and functional losses. However, understanding the advances and trends in soil MP pollution research proved to be difficult based on individual studies. Herein, CiteSpace visualization analysis software was used to sort the literature obtained on soil MP research from the Web of Science (WoS) TM core collection database, and bibliometric analysis of publication volume, country/region, keywords and other information was conducted. Results showed that soil microplastics (MP) research had much lower publication volume than hydrological system MP research, and was still in its developing stage. However, the number of research hotspots in soil MP research increased continuously since 2018. Countries such as China, the United States, the Netherlands and Australia have published more papers in this field than other countries, and collaboration among research institutions is strong. The source, pollution status, analysis methods and toxic effects of MPs in soils are current research hotspots. In the future, the focus of MP research in terrestrial ecosystems should shift from ecotoxicology to the impact of MPs on ecosystems and earth system feedback. The bibliometric analysis results of this study can serve as a reference for in-depth studies of the pollution status of MPs in soils. For MP soil pollution research, cross-background, cross-institution, cross-country cooperation and cross-disciplinary roles should be encouraged to accelerate the growth and diversification of the field. [ABSTRACT FROM AUTHOR]

This paper uses the bibliometric method of knowledge mapping analysis to clearly present the knowledge base and research fronts of cross-market volatility spillovers. The results provide strong evidence that, first, the general theme of volatility spillovers can be divided into a variety of research topics, four of which are on the dynamics of volatility spillovers in world financial markets of various types based on multivariate GARCH or VAR models and construct a crucial knowledge base for this field; second, three research fronts can be identified using burst analysis, and they focus on examining spillover directions and magnitudes, testing volatility spillovers related to oil markets and international risk transmission mechanism of emerging markets; and, third, the major contributing scholars come from institutions in the United States,China and European economies. Our conclusions offer some recommendations for market practitioners in their risk management and policy-making. [ABSTRACT FROM AUTHOR]

Priority needs to be given to the green technology of saving resources, saving energy and reducing pollution by enterprises in the new era where green development needs promotion because humans and nature are the community of life. In essence, green technology is the innovation-driven development of enterprises under the background of green development. In order to sort out the research trend of Chinese scholars on green technology, 206 articles published during the period of 1994 to 2017 and their citations mainly from the Chinese Social Sciences Citation Index database were studied by means of bibliometrics and mapping knowledge domains, which reveals the trend of a number of Chinese green technology research papers published, the evolution path of the hot research topics, the research frontier and the knowledge base, drawing inspiration from them. The main conclusions are as follows: 1) The number of Chinese scholars' research on green technology shows an N-type upward trend and will continue to rise in the future. 2) Green technology, technological innovation and environmental protection have long been research hotspots of China's green technology research. 3) The frontiers of green technology research in China can be divided into three stages for germination (1994 to 2010), rapid growth (2010 to 2014)and depth advancement (2015 to present. 4) The knowledge base of green technology research in China is mainly composed of efficiency, the relationship between the environment and economics, green technological innovation, environmental regulation and intellectual property. [ABSTRACT FROM AUTHOR]

Social networks are increasingly considered to be influential in explaining the knowledge transfer process. Despite scholarly efforts to integrate knowledge transfer and social network research, we lack understanding on how knowledge transfer networks emerge and evolve. We draw upon resource dependency theory and inter-organizational networks and collect patent data of 42 Double-First Class (DFC) universities to study structural properties of the Chinese university knowledge transfer network over time. Our results point to the existence of an increasingly complex yet remarkably efficient network. Universities and co-patent collaborations emerge in the network and act as knowledge bridges between other universities. The network moves from an early-stage single-centered network to a mature multi-centered network through a power-law pattern. Such movement allows for an aggregation phenomenon to appear as oligopolistic communities emerge and rule the network. While knowledge is more easily shared and accessible within communities, their existence also brings along control over knowledge bases embedded in those communities. Key universities take central positions within the expanding network, which allows them to gain control and easier access to knowledge. It also hints that it might be difficult for other DFC universities to become key players in the network. On an inter-regional level, our findings point to steadily increasing knowledge transfer activity, which is key to overcome the underdevelopment of some Chinese regions. Overall, this paper contributes to our understanding on the theoretical connection between knowledge transfer and social network dynamics, on how universities evolve through knowledge transfer networks, and on how their embeddedness translates into knowledge control, knowledge access, and knowledge bridges. [ABSTRACT FROM AUTHOR]

What role has science played in China's decision to support and ratify the Minamata Convention, and in its domestic policies on mercury? Since 2000 there has been a strong increase in internationally produced knowledge on mercury and China has increasingly taken part in this process, also developing a stronger domestic knowledge base for handling the mercury problem. We analyse three aspects of science that are assumed to enhance trust: credibility, legitimacy, and relevance. These are explored in the study of science-policy relations in China as we examine changes in domestic mercury policies and related institutions. Also discussing the effects of other explanatory factors, we find that domestically produced scientific information may be central for understanding China's ratification of the Minamata Convention and subsequent domestic mercury policies. The study bridges the gap between capacity building in emerging economies and how domestically produced scientific information may strengthen national environmental policy making. [ABSTRACT FROM AUTHOR]

From a knowledge-based perspective, this paper highlights the need for a framework linking perceived competition of the industry and firms' strategic location choices of external knowledge search and examines whether the perceived competition increases the propensity of cluster firms to search more widely from extra-cluster knowledge sources than intra-cluster knowledge sources. Furthermore, we emphasise that cluster firms with varying degrees of tacitness in their knowledge base might respond to such competition differently when conducting external knowledge search activities. Using a sample of 310 cluster firms in the Zhejiang Province of China, we find that the cluster firm would increase the propensity for more geographic boundary-spanning search relative to local search while under the pressure of competition. Moreover, the positive relationship between perceived competition and the propensity towards geographic knowledge search is weaker when the tacitness of the cluster firm's knowledge base is higher. The findings contribute to the understanding of the relationship between competition and the choice of location strategies in external knowledge search. [ABSTRACT FROM AUTHOR]

As an important indicator of greenhouse gas emissions, the carbon footprint (CF) has become increasingly important in recent years under the dual pressures of global warming and international commitments to mitigate its effects. This study collected 3698 papers related to CF from the Web of Science database as research samples (year 2007 to 2018). Based on CiteSpace, the knowledge base, popular topics, and research trends of CF are presented. The results show the following: (1) from 2007 to 2018, the number of articles on CF have steadily increased. (2) After spatial analysis of the literature, we found that among research institutions, the Chinese Academy of Sciences has the largest number of publications on the issue. When it comes to country, three important research forces can be identified: USA, China, and UK. (3) Research on the CF is interdisciplinary; in addition to the traditional fields of environmental, political, economics, and computing, CF research has received attention from the Physics, Materials, Chemistry, Mathematics, and animal sciences. (4) Through keyword clustering, currently popular topics in research can be roughly divided into four aspects: CF calculation methods, research scales, energy, and agriculture. (5) The CF research during the study period is divided into four stages according to the burst time and content of the burst keywords. According to the research status and trend, this paper puts forward the future research direction of carbon footprint. [ABSTRACT FROM AUTHOR]

• A visual analysis was performed on 5814 articles of cerebral ischemia-reperfusion(IR) from 2004 to 2019. • This article describes the spatial and temporal distribution of research resources for IR. • The knowledge bases of IR have focused on five major areas. • The research focuses consist of three representative areas. Cerebral ischemia-reperfusion (IR) has attracted wide attention as a serious clinical problem. So far, the field has accumulated a large amount of scientific research literature. To clarify the temporal and spatial distribution characteristics of research resources, knowledge bases and research focuses, a visual analysis was performed on 5814 articles cited in the WoS databases from 2004 to 2019. This analysis was based on bibliometrics and mapping knowledge domain (MKD) analysis with VOSviewer, and CiteSpace 5.4.R4. The results can be elaborated from four aspects. First, the volume of publications in this area is on the rise. Second, the United States and China are the active regions. The USA is the central region of cerebral ischemia-reperfusion research. Third, the knowledge bases of IR have focused on five major areas of "Suitable small-animal models", "A framework with further study", "Molecular signaling targets by oxidative stress", "Finding new potential targets for therapy" and "Protective effect of multiple transient ischemia". Fourth, the research focuses consist of three representative areas: "Oxidative stress closelyd with cerebral ischemia-reperfusion", "Neuronal apoptosis and neuronal protection", and "Neuroprotective effect of the blood-brain barrier". [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to investigate the relationships between two types of informal ties and radical innovation in the context of China's university spin-offs and the moderating roles of knowledge breadth and depth in such relationships. Design/methodology/approach: A systematic review of literature on informal ties, internal knowledge base and radical innovation provides the theoretical foundation of the research framework and hypotheses. Using a sample of 158 China's university spin-offs, the authors conduct a regression analysis on the theoretical framework and hypotheses. Findings: The results show that business and university ties are positively related to radical innovation. Moreover, the effects of business and university ties on radical innovation are contingent on knowledge breadth and depth in opposite ways. In particular, the positive effect of business ties on radical innovation depends significantly on internal knowledge depth rather than on knowledge breadth, and the positive effect of university ties on radical innovation will be affected by internal knowledge breadth rather than knowledge depth. Practical implications: Managers of university spin-offs must examine informal ties they already have and identify their nature, content and embedded advantages and promptly adjust their strategy of informal ties to adapt to their firms' internal knowledge base. Originality/value: This study highlights the positive role of managers' personal connections with different external parties in facilitating radical innovation and advances the understanding of informal ties by proposing that the effects of informal ties on radical innovation are contingent on a firm's internal knowledge base in the context of China's university spin-offs. [ABSTRACT FROM AUTHOR]

The aim of this paper is to describe an international project aimed at building a knowledge base between Canada and China and beyond for cross-cultural educational understanding and harmonious relationships. The overall purpose of this work is to move beyond cross-cultural comparison and competition to collaborative reciprocity. Reciprocal learning is the guiding term for the Canada–China Partnership Project. The term is relatively commonplace and its deceptive simplicity veils important research and public discourse consequences. The Partnership Project, though not explicitly comparative in nature, draws heavily on the literature of comparative education, particularly with respect to methodology and understandings of Chinese and Canadian education. We describe four comparative education approaches which we think of as models of reciprocal learning across cultures. In the following remarks, we unpack these ideas of reciprocal learning as they apply to collaborative studies of Chinese and Canadian education. The models are assessed for their potential to generate collaborative teaching and learning situations with associated reciprocal learning. [ABSTRACT FROM AUTHOR]

Technology upgrading is essential for emerging countries to catch up. Yet, little is known about how countries are upgrading their technologies. In this paper, we first explore the patterns of biotechnology upgrading in China and examine the specialization feature from both technological and organizational perspectives. Our results show that China has specialized in the industrial-biotech sub-field, and firms have played a key role in the development of biotech in this field. Secondly, we investigate the knowledge bases for technology upgrading. From the perspective of scientific knowledge bases, we find that there is a scientific backup for biotechnology; and that biotechnology goes hand in hand with bioscience. From the perspective of technical knowledge bases, by disaggregating the knowledge sources by countries, we find that local knowledge has become an increasingly important resource for the development of new biotechnologies in China. The increasing importance of local knowledge in the development of new biotechnologies shows that China is moving on a path from imitative to indigenous innovation. [ABSTRACT FROM AUTHOR]

There is a huge gap between the conceptual design and the practical implementation of the conservation planning of traditional architecture and settlement in China. The major cause is the diversity of individual comprehension. In this study, a generative design method was developed to regenerate the facades. The proposed method first adapted novel data acquisition and processing techniques to gather information and build the database. Cognition investigation and morphology analysis were then implemented to quantify and evaluate the features of historical characteristics. The knowledge discovery tools were further used to abstract the rules of the traditional facade. With the above three phases, the proposed method was able to generate the referable facades quantitatively and establishing generally accepted conservation plans and guidelines. [ABSTRACT FROM AUTHOR]