Identification and Prediction of Interdisciplinary Research Topics: A Study Based on the Concept Lattice Theory

Purpose: Formal concept analysis(FCA) and concept lattice theory(CLT) are introduced for constructing a network of IDR topics and for evaluating their effectiveness for knowledge structure exploration.Design/methodology/approach: We introduced the theory and applications of FCA and CLT, and then proposed a method for interdisciplinary knowledge discovery based on CLT. As an example of empirical analysis, interdisciplinary research(IDR) topics in Information & Library Science(LIS) and Medical Informatics, and in LIS and Geography-Physical, were utilized as empirical fields. Subsequently, we carried out a comparative analysis with two other IDR topic recognition methods.Findings: The CLT approach is suitable for IDR topic identification and predictions.Research limitations: IDR topic recognition based on the CLT is not sensitive to the interdisciplinarity of topic terms, since the data can only reflect whether there is a relationship between the discipline and the topic terms. Moreover, the CLT cannot clearly represent a large amounts of concepts.Practical implications: A deeper understanding of the IDR topics was obtained as the structural and hierarchical relationships between them were identified, which can help to get more precise identification and prediction to IDR topics.Originality/value: IDR topics identification based on CLT have performed well and this theory has several advantages for identifying and predicting IDR topics. First, in a concept lattice, there is a partial order relation between interconnected nodes, and consequently, a complete concept lattice can present hierarchical properties. Second, clustering analysis of IDR topics based on concept lattices can yield clusters that highlight the essential knowledge features and help display the semantic relationship between different IDR topics. Furthermore, the Hasse diagram automatically displays all the IDR topics associated with the different disciplines, thus forming clusters of specific concepts and visually retaining and presenting the associations of IDR topics through multiple inheritance relationships between the concepts.

An SAO-based approach to technology evolution analysis using patent information: Case study—graphene sensors

Purpose: This paper introduces an approach to technology evolution analysis using patent information based on Subject-Action-Object(SAO) structures.Design/methodology/approach: First, SAO structures were extracted from patent documents and were cleaned. Second, several dimension-reduction techniques were used to generate technology topics. Then, the hierarchical relationships between technology topics were calculated based on patent clustering. Finally, technology evolution maps were drawn by adding a timeline.Findings: This approach can reveal technology evolution processes from multiple perspectives than other approaches.Research limitations: The semantic annotation of an SAO type is not very accurate and the semantic types of technology topics need to be enriched.Practical implications: The approach can be applied to draw technology evolution maps using different types of technology topics such as problem or solution.Originality/value: This approach offers an analytical dimension and more details than existing techniques, and it helps identify fundamental and emerging technologies more accurately and comprehensively.

A Study of Methods to Identify Industry-University-Research Institution Cooperation Partners based on Innovation Chain Theory

Purpose： This study aims at identifying potential industry-university-research collaboration（IURC） partners effectively and analyzes the conditions and dynamics in the IURC process based on innovation chain theory.Design/methodology/approach： The method utilizes multisource data, combining bibliometric and econometrics analyses to capture the core network of the existing collaboration networks and institution competitiveness in the innovation chain. Furthermore, a new identification method is constructed that takes into account the law of scientific research cooperation and economic factors.Findings： Empirical analysis of the genetic engineering vaccine field shows that through the distribution characteristics of creative technologies from different institutions, the analysis based on the innovation chain can identify the more complementary capacities among organizations.Research limitations： In this study, the overall approach is shaped by the theoretical concept of an innovation chain, a linear innovation model with specific types or stages of innovation activities in each phase of the chain, and may, thus, overlook important feedback mechanisms in the innovation process.Practical implications： Industry-university-research institution collaborations are extremely important in promoting the dissemination of innovative knowledge, enhancing the quality of innovation products, and facilitating the transformation of scientific achievements.Originality/value： Compared to previous studies, this study emulates the real conditions of IURC. Thus, the rule of technological innovation can be better revealed, the potential partners of IURC can be identified more readily, and the conclusion has more value.

Topic Detection Based on Weak Tie Analysis： A Case Study of LIS Research

Purpose: Based on the weak tie theory, this paper proposes a series of connection indicators Acof weak tie subnets and weak tie nodes to detect research topics, recognize their connections, and understand their evolution.Design/methodology/approach: First, keywords are extracted from article titles and preprocessed. Second, high-frequency keywords are selected to generate weak tie co-occurrence networks. By removing the internal lines of clustered sub-topic networks, we focus on the analysis of weak tie subnets’ composition and functions and the weak tie nodes’ roles.Findings: The research topics’ clusters and themes changed yearly; the subnets clustered with technique-related and methodology-related topics have been the core, important subnets for years; while close subnets are highly independent, research topics are generally concentrated and most topics are application-related; the roles and functions of nodes and weak ties are diversified.Research limitations: The parameter values are somewhat inconsistent; the weak tie subnets and nodes are classified based on empirical observations, and the conclusions are not verified or compared to other methods.Practical implications: The research is valuable for detecting important research topics as well as their roles, interrelations, and evolution trends. Originality/value: To contribute to the strength of weak tie theory, the research translates weak and strong ties concepts to co-occurrence strength, and analyzes weak ties’ functions. Also, the research proposes a quantitative method to classify and measure the topics’ clusters and nodes.

The current and future of terrestrial carbon balance over the Tibetan Plateau

The contemporary carbon balance over the Tibetan Plateau is highly uncertain with a ten-fold difference between various estimates.In a warming world,the potential exists for a large carbon release from its permafrost which could compromise China’s 2060 carbon-neutral goal.Here,we used a satellite-and inventory-based approach,ecosystem models,and atmospheric inversions to estimate that the carbon sink was 33.12–37.84 TgC yr^(–1)during 2000–2015.The carbon sink induced by climate change and increasing CO_(2) levels largely overcompensated for a livestock grazing-induced carbon source of 0.38TgC yr^(-1).By 2060,the carbon sink is projected to increase by 38.3–74.5% under moderate to high emissions scenarios,with the enhanced vegetation carbon uptake outweighing the warming-induced permafrost carbon release.The restoration of degraded grassland could sequestrate an additional 9.06 TgC yr^(-1),leading to a total carbon sink of 57.78–70.52 TgC yr^(-1).We conclude that the Tibetan Plateau’s ecosystems absorbed two-and-a-half times the amount of its cumulative fossil CO_(2) emissions during 2000–2015 and that their carbon sinks will almost double in strength in the future,helping to achieve China’s pledge to become carbon neutral by 2060.

Amino acids industry knowledge service platform

Purpose： This paper introduces an amino acids industry knowledge service platform, which integrates industry data, science literature, and technology knowledge from patents.Design/methodology/approach： From the perspective of knowledge organization, the amino acids industry knowledge service platform comprises a basic data layer, a knowledge representation layer, and a knowledge service layer. The platform integrates heterogeneous information sources by means of a multi-dimensional integration index. It enables users to find technology knowledge from patents by employing the semantic Theory of Inventive Problem Solving （TRIZ）.Findings： This paper constructs an integrated amino acids industry database, which offers a general service oriented to the public, and a technical innovation knowledge base, which offers a value-added service oriented to enterprises.Research limitations： The technical innovation knowledge base only includes parts of amino acids technology knowledge, making it suitable to meet certain specific needs.Practical implications： Semantic annotation of technical innovation knowledge from patents requires manual work by domain experts.Originality/value： The amino acids industry knowledge service platform offers not only an integrated search service for amino acids industry information, but also a deep tech mining service for technical innovation knowledge of amino acids. It will offer a more personalized knowledge service in the future.

High-throughput,microscopy-based screening and quantification of genetic elements

Synthetic biology relies on the screening and quantification of genetic components to assemble sophisticated gene circuits with specific functions.Microscopy is a powerful tool for characterizing complex cellular phenotypes with increasing spatial and temporal resolution to library screening of genetic elements.Microscopy-based assays are powerful tools for characterizing cellular phenotypes with spatial and temporal resolution and can be applied to large-scale samples for library screening of genetic elements.However,strategies for high-throughput microscopy experiments remain limited.Here,we present a high-throughput,microscopy-based platform that can simultaneously complete the preparation of an 8×12-well agarose pad plate,allowing for the screening of 96 independent strains or experimental conditions in a single experiment.Using this platform,we screened a library of natural intrinsic promoters from Pseudomonas aeruginosa and identified a small subset of robust promoters that drives stable levels of gene expression under varying growth conditions.Additionally,the platform allowed for single-cell measurement of genetic elements over time,enabling the identification of complex and dynamic phenotypes to map genotype in high throughput.We expected that the platform could be employed to accelerate the identification and characterization of genetic elements in various biological systems,as well as to understand the relationship between cellular phenotypes and internal states,including genotypes and gene expression programs.