Search results

This study aims to establish a reliable index to identify interdisciplinary breakthrough innovation effectively. We constructed a new index, the D_Div index, for this purpose.

The D_Div index incorporates the degree of interdisciplinarity in the breakthrough index. To validate the index, a data set combining the publication records and citations of Nobel Prize laureates was divided into experimental and control groups. The validation methods included sensitivity analysis, correlation analysis and effectiveness analysis.

The sensitivity analysis demonstrated the D_Div index’s ability to differentiate interdisciplinary breakthrough papers from various categories of papers. This index not only retains the strengths of the existing index in identifying breakthrough innovation but also captures interdisciplinary characteristics. The correlation analysis revealed a significant correlation (correlation coefficient = 0.555) between the interdisciplinary attributes of scientific research and the occurrence of breakthrough innovation. The effectiveness analysis showed that the D_Div index reached the highest prediction accuracy of 0.8. Furthermore, the D_Div index outperforms the traditional DI index in terms of accuracy when it comes to identifying interdisciplinary breakthrough innovation.

This study proposed a practical and effective index that combines interdisciplinary and disruptive dimensions for detecting interdisciplinary breakthrough innovation. The identification and measurement of interdisciplinary breakthrough innovation play a crucial role in facilitating the integration of multidisciplinary knowledge, thereby accelerating the scientific breakthrough process.

This study aims to evaluate a method of building a biomedical knowledge graph (KG).

This research first constructs a COVID-19 KG on the COVID-19 Open Research Data Set, covering information over six categories (i.e. disease, drug, gene, species, therapy and symptom). The construction used open-source tools to extract entities, relations and triples. Then, the COVID-19 KG is evaluated on three data-quality dimensions: correctness, relatedness and comprehensiveness, using a semiautomatic approach. Finally, this study assesses the application of the KG by building a question answering (Q&A) system. Five queries regarding COVID-19 genomes, symptoms, transmissions and therapeutics were submitted to the system and the results were analyzed.

With current extraction tools, the quality of the KG is moderate and difficult to improve, unless more efforts are made to improve the tools for entity extraction, relation extraction and others. This study finds that comprehensiveness and relatedness positively correlate with the data size. Furthermore, the results indicate the performances of the Q&A systems built on the larger-scale KGs are better than the smaller ones for most queries, proving the importance of relatedness and comprehensiveness to ensure the usefulness of the KG.

The KG construction process, data-quality-based and application-based evaluations discussed in this paper provide valuable references for KG researchers and practitioners to build high-quality domain-specific knowledge discovery systems.

In a people-oriented society, it is necessary to understand the electronic word of mouth (eWOM) of information service of the government. Government affair microblogs (GAM), which play an important role in orienting online opinions, listening to public voices, establishing government image, is an ideal channel to achieve this goal. In essence, the application of GAM is to improve the communication efficiency between the government and the public. The purpose of this study is to the influence factors of GAM eWOM which will not only benefits the government in better understanding the information dissemination rules of microblogs but also provides directions for the government to improve the information service over microblogs.

In this paper, the authors investigate and verify the regulating function, herd effect applied on GAM. The authors conduct an innovative empirical study based on perceived value theory, aiming to explore the factors that influence the eWOM of the Chinese Government.

Experiments on 502 GAM users show that social identification positively affects perceived value while effort cost negatively affects perceived value, and perceived value positively influences the microblogs’ eWOM. The authors also find that the herd effect positively regulates the relationship between perceived value and GAM’s eWOM. Theoretical and practical implications of these findings are discussed.

In this research, the regulating effect of herd effect on the relationship between perceived value and GAM’s eWOM has been explored. This study also provides inspirations for future government information service research and administrative measurement.

The purpose of this paper is to provide an integrated semantic information retrieval (IR) solution based on an ontology-improved vector space model for situations where a digital collection is established or curated. It aims to create a retrieval approach which could return the results by meanings rather than by keywords.

In this paper, the authors propose a semantic term frequency algorithm to create a semantic vector space model (SeVSM) based on ontology. To support the calculation, a multi-branches tree model is created to represent the ontology and a set of algorithms is developed to operate it. Then, a semantic ontology-based IR system based on the SeVSM model is designed and developed to verify the effectiveness of the proposed model.

The experimental study using 30 queries from 15 different domains confirms the effectiveness of the SeVSM and the usability of the proposed system. The results demonstrate that the proposed model and system can be a significant exploration to enhance IR in specific domains, such as a digital library and e-commerce.

This research not only creates a semantic retrieval model, but also provides the application approach via designing and developing a semantic retrieval system based on the model. Comparing with most of the current related research, the proposed research studies the whole process of realizing a semantic retrieval.

Citation contexts have been found useful in many scenarios. However, existing context-based recommendations ignored the importance of diversity in reducing the redundant issues and thus cannot cover the broad range of user interests. To address this gap, the paper aims to propose a novelty task that can recommend a set of diverse citation contexts extracted from a list of citing articles. This will assist users in understanding how other scholars have cited an article and deciding which articles they should cite in their own writing.

This research combines three semantic distance algorithms and three diversification re-ranking algorithms for the diversifying recommendation based on the CiteSeer^X data set and then evaluates the generated citation context lists by applying a user case study on 30 articles.

Results show that a diversification strategy that combined “word2vec” and “Integer Linear Programming” leads to better reading experience for participants than other diversification strategies, such as CiteSeer^X using a list sorted by citation counts.

This diversifying recommendation task is valuable for developing better systems in information retrieval, automatic academic recommendations and summarization.

The originality of the research lies in the proposal of a novelty task that can recommend a diversification context list describing how other scholars cited an article, thereby making citing decisions easier. A novel mixed approach is explored to generate the most efficient diversifying strategy. Besides, rather than traditional information retrieval evaluation, a user evaluation framework is introduced to reflect user information needs more objectively.

The purpose of this paper is to fill important gaps by using the belongingness theory and examining the effects of individual-level workplace ostracism on members’ voice behavior and the effects of group-level workplace ostracism.

The authors used samples of 77 groups from a high-technological company.

Individual-level workplace ostracism is detrimental to group members’ promotive and prohibitive voice behavior, and group members’ belongingness mediates such effect; and group-level workplace ostracism is negatively related to group cohesion. The influence of group members’ perception of ostracism on their voice behavior is contingent on overall level of ostracism.

Despite of a growing body of studies on workplace ostracism “the extent to which an individual perceives that he or she is ignored or excluded by others at workplace”, the effects of workplace ostracism on individual’s voice behavior in group settings have received scant attention.

The purpose of this paper is to bring up the concept of multi‐material electromagnetic band‐gap structure (EBGs) and develop a method for its fabrication. Meanwhile, its microwave properties were studied and compared with the traditional EBGs consisting of two kinds of material.

Stereolithography (SL) and gel casting were used to fabricate 3D multi‐material EBGs. Resin mold was designed and fabricated based on SL process, slurries loaded with 55vol per cent Al₂O₃ and 55vol per cent TiO₂, respectively, were prepared, and using gel casting, multilayer EBGs with diamond structure were fabricated. T/R method was used to obtain the characteristic parameter S₂₁ of the EBGs; meanwhile, characters of their band structure were studied based on plane wave expansion method.

The fabricated EBGs with a TiO₂‐resin‐air structure showed a band gap from 11.7 GHz to 16.0 GHz along <1, 1, 0> direction; the EBGs with a TiO₂‐resin‐Al₂O₃ structure showed a band gap from 11.4 GHz to 11.9 GHz along <1, 1, 0> direction. Both of them agreed well with the simulation result. Also, through the study of multi‐material EBGs' microwave properties, it could be seen that this structure was a good approach to adjust the band gap.

With the concept of multi‐material EBG structure brought up, multilayer 3D EBGs were designed and fabricated based on SL combined with gel casting. It could be seen that multi‐material EBGs was a good approach to adjust the band gap. Also, the fact that the testing result matched the simulation validates the feasibility of the process.

This study aims to empirically examine the relationships among perceived environmental uncertainty (EV), the level of knowledge distance (KD) and the impact of value network on firm performance.

The quantitative analysis is based on data from 243 Chinese companies with engineering, procurement and construction (EPC) business in the context of the COVID-19 pandemic.

The two dimensions of value network [network centrality (NC) and network openness (NO)] have a different impact on firm performance [financial performance (FP) and market performance (MP)]. NC has a positive impact on FP, but not on MP. NO has a positive effect on MP, but not on FP. A reduced KD mediates the relationship between value network and firm performance. Moreover, it fully mediates the relationship between NC and MP, NO and FP. Finally, during the COVID-19 pandemic, only EV has a moderating effect on KD and MP.

This study is limited in terms of data set because it relies on a limited amount of cross-sectional data from one specific country. Therefore, researchers are encouraged to test the proposed propositions further.

The present findings suggest that EPC professionals should pay more attention to the EV, which may be impacted by policy, technology and the economy. This research has actionable implications for the reform of EPC in the construction industry, and practical recommendations for EPC firms to improve their corporate performance.

The results measure the complementary effects of both dimensions of value network (NC and NO) on two distinct aspects of firm performance (MP and FP) and assess the moderating effect of EV and KD in the context of the COVID-19 pandemics.

The purpose of this paper is to develop a novel process of integral ceramic molds for investment casting of hollow turbine blades.

At first, a resin pattern of a hollow turbine blade prototype is fabricated by stereolithography (SL). And then aqueous gelcasting process is utilized to fill the resin pattern with ceramic slurry of low viscosity and low shrinkage, through in situ polymerization of the slurry a ceramic mold is formed. At last, the ceramic mold for investment casting of hollow turbine blade is obtained by vacuum drying, pyrolyzing and sintering.

An integral ceramic mold is successfully fabricated by combining SL and gelcasting process, cores and shell are connected with each other and thus high relative position accuracy is guaranteed. Properties of integral ceramic mold at room temperature and high temperature satisfy the requirements of directional casting for complex‐shaped thin‐walled blades.

Because the integral ceramic mold is a close body, it is very difficult to directly measure its inner dimensions and the relative position accuracy of cores and shell, and the further research is needed.

This method enhanced the versatility of using SL prototype in the fabrication of integral ceramic mold for investment castings. Although this paper took a hollow turbine blade as an example, this method is also capable of fabricating integral ceramic molds for other complex investment castings.

With the continuous upgrading of the production mode of the manufacturing system, the characteristics of multi-variety, small batch and mixed fluidization are presented, and the production environment becomes more and more complex. To improve the efficiency of solving multi-objective flexible job shop scheduling problem (FJSP), an improved hybrid particle swarm optimization algorithm (IH-PSO) is proposed.

After reviewing literatures on FJSP, an IH-PSO algorithm for solving FJSP is developed. First, IH-PSO algorithm draws on the crossover and mutation operations of genetic algorithm (GA) algorithm and proposes a new method for updating particles, which makes the offspring particles inherit the superior characteristics of the parent particles. Second, based on the improved simulated annealing (SA) algorithm, the method of updating the individual best particles expands the search scope of the domain and solves the problem of being easily trapped in local optimum. Finally, analytic hierarchy process (AHP) is used in this paper to solve the optimal solution satisfying multi-objective optimization.

Through the benchmark experiment and the production example experiment, it is verified that the proposed algorithm has the advantages of high quality of solution and fast speed of convergence.

This method does not consider the unforeseen events that occur during the process of scheduling and cause the disruption of normal production scheduling activities, such as machine breakdown.

IH-PSO algorithm combines PSO algorithm with GA and SA algorithms. This algorithm retains the advantage of fast convergence speed of traditional PSO algorithm and has the characteristic of inheriting excellent genes. In addition, the improved SA algorithm is used to solve the problem of falling into local optimum.

This research provides an efficient scheduling method for solving the FJSP problem.

This research proposes an IH-PSO algorithm to solve the FJSP more efficiently and meet the needs of multi-objective optimization.