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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.

Aspects extracted from the user’s historical records are widely used to define user’s fine-grained preferences for building interpretable recommendation systems. As the aspects were extracted from the historical records, the aspects that represent user’s negative preferences cannot be identified because of their absence from the records. However, these latent aspects are also as important as those aspects representing user’s positive preferences for building a recommendation system. This paper aims to identify the user’s positive preferences and negative preferences for building an interpretable recommendation.

First, high-frequency tags are selected as aspects to describe user preferences in aspect-level. Second, user positive and negative preferences are calculated according to the positive and negative preference model, and the interaction between similar aspects is adopted to address the aspect sparsity problem. Finally, an experiment is designed to evaluate the effectiveness of the model. The code and the experiment data link is: https://github.com/shiyu108/Recommendation-system

Experimental results show the proposed approach outperformed the state-of-the-art methods in widely used public data sets. These latent aspects are also as important as those aspects representing the user’s positive preferences for building a recommendation system.

This paper provides a new approach that identifies and uses not only users’ positive preferences but also negative preferences, which can capture user preference precisely. Besides, the proposed model provides good interpretability.

The disbonding of DLC coating is a main failure mode in the high-speed cavitation condition, which shortens the service life of the bearing. This study aims to investigate influence of adhesion strength on cavitation erosion resistance of DLC coating.

Three DLC coatings with different adhesion strengths were grown on the 304 steel surfaces by using a cathodic arc plasma deposition method. Cavitation tests were performed by using a vibratory test rig to investigate the influence of adhesion strength on cavitation erosion resistance of a DLC coating. The cavitation mechanism of the substrate-coating systems was further discussed by means of surface analyses.

The results indicated that, the residual stress decreased and then increased with the increasing DLC coating thickness from 1 µm to 2.9 µm, and the lower residual stress can improve the adhesion strength of the DLC coating to the substrate. It was also concluded that, the plastic deformation as well as the fracture occurred on the DLC coating surface at the same time, owing to higher residual stress and poorer adhesion strength. However, lower residual stress and better adhesion strength could help resist the occurrence of the coating fracture.

Cavitation tests were performed by using a vibratory test rig to investigate the influence of adhesion strength on cavitation erosion resistance of the DLC coating. The plastic deformation and the fracture occurred on the DLC coating surface at the same time, owing to higher residual stress and poorer adhesion of coating. Lower residual stress and better adhesion of coating could resist the occurrence of the DLC coating fracture.

The purpose of this paper is to provide additive manufacturing-based solutions for preparation of elastomeric foam with broaden compressive stress plateau.

Mechanic models are developed for obtaining designs of foam cell units with enhanced elastic buckling. An experimental approach is taken to fabricate the foams based on direct ink writing technique. Experimental and simulation data are collected to assist understanding of our proposals and solutions.

A simple tetragonal structured elastomeric foam is proposed and fabricated by direct ink writing, in which its cell unit is theoretically designed by repeating every four filament layers. The foam exhibits a broader stress plateau, because of the pronounced elastic buckling under compressive loading as predicted by the authors’ mechanic modeling. A two-stage stress plateaus as observed in the foam, being attributed to the dual elastic buckling of the cell units along two lateral directions of the XY plane during compression.

Future work should incorporate more microscopic parameters to tune the elastomeric foam for mechanic performance testing on linear elastic deformation and densification of polymer matrix.

Additive manufacturing offers an alternative to fabricate elastomeric foam with controlled cell unit design and therefore mechanics. Our results comment on its broad space for development such superior cushioning or damping material in the fields of vibration and energy absorption.

This work has contributed to new knowledge on preparation of high performance elastomeric foam by providing a better understanding on its cell structure, being printed using direct ink writing machines.

Efficient traffic incident management is needed to alleviate the negative impact of traffic incidents. Accurate and reliable estimation of traffic incident duration is of great importance for traffic incident management. Previous studies have proposed models for traffic incident duration prediction; however, most of these studies focus on the total duration and could not update prediction results in real-time. From a traveler’s perspective, the relevant factor is the residual duration of the impact of the traffic incident. Besides, few (if any) studies have used dynamic traffic flow parameters in the prediction models. This paper aims to propose a framework to fill these gaps.

This paper proposes a framework based on the multi-layer perception (MLP) and long short-term memory (LSTM) model. The proposed methodology integrates traffic incident-related factors and real-time traffic flow parameters to predict the residual traffic incident duration. To validate the effectiveness of the framework, traffic incident data and traffic flow data from Shanghai Zhonghuan Expressway are used for modeling training and testing.

Results show that the model with 30-min time window and taking both traffic volume and speed as inputs performed best. The area under the curve values exceed 0.85 and the prediction accuracies exceed 0.75. These indicators demonstrated that the model is appropriate for this study context. The model provides new insights into traffic incident duration prediction.

The incident samples applied by this study might not be enough and the variables are not abundant. The number of injuries and casualties, more detailed description of the incident location and other variables are expected to be used to characterize the traffic incident comprehensively. The framework needs to be further validated through a sufficiently large number of variables and locations.

The framework can help reduce the impacts of incidents on the safety of efficiency of road traffic once implemented in intelligent transport system and traffic management systems in future practical applications.

This study uses two artificial neural network methods, MLP and LSTM, to establish a framework aiming at providing accurate and time-efficient information on traffic incident duration in the future for transportation operators and travelers. This study will contribute to the deployment of emergency management and urban traffic navigation planning.

The purpose of the paper is to investigate compensation and related welfare issues in the case of the expropriation of land for urban redevelopment in China.

A mixed methods quantitative and qualitative approach was selected to undertake the research. This involved a wide ranging qualitative review of the academic and policy literature to explore the relevant arguments and issues, combined with a quantitative regression analysis of survey data collected from research subjects.

The research identified the complex and changeable phenomena of urban village redevelopment in China, and the variable compensation arrangements used. The research found that monthly family income before land expropriation, monthly family expense before expropriation, the location of the housing expropriation and family unit size are important determinants for the property holders chosen methods of compensation. It also found that an increase in family size leads to a decreasing probability that the expropriated farmers choose the single monetary compensation relative to the alternative option of housing compensation. The degree of satisfaction with compensation, changes in monthly family income and expense are found to be significant determinants for changes in life satisfaction.

The research made the following four recommendations based upon the qualitative and quantitative analysis: that local governments should pay closer governance/ political attention to changes in the welfare of the farmers/ villagers whose property has been expropriated; that central and local government should aim to improve the compensation system for rural land and property expropriation, to make the compensation policy be perceived as fairer and more reasonable by citizens; that a broad National standard of compensation be used within a pragmatic locally focussed regime; that the Chinese Central, Provincial and Local governments can devise improved policy tools and make more effective policy interventions by learning from the experiences (both successes and failures) of other countries approaches to this topic. It also suggested that further research be undertaken investigating the multitude of local level policy experiments, as a way of developing better National compensation standards based upon those compensation standards that appear to be working – and have citizen support – at the local level.

The literature review identified recent developments in Chinese urban studies and originally synthesised both recent and longstanding work on the issue of urban villages in China. The research also suggested changes to the National and Local legal and policy framework for compensation cases in urban redevelopment expropriation scenarios.

This study aimed to identify barriers impeding circular economy (CE) uptake in the construction industry in literature, categorize them for the development of a framework and to seek the interrelationships among the categorized barriers. This allowed for identifying integrated solutions to holistically address the barriers. The study also sought to identify the “hot” themes, the knowledge gaps and future research directions on barriers impeding CE.

Forty-eight relevant articles were desk reviewed from different construction peer-reviewed journals and published conference papers. A scientometric analysis allowed for co-occurrence of keywords relating to CE. A content analysis enabled the identification of 79 barriers impeding the uptake of CE in the construction industry which were further categorized into six distinct categories for the development of a framework showing the interrelationships among the categorized barriers.

The identified barriers include construction sector inertia, lack of design standards, lack of knowledge, awareness and understanding, design cost, and perception of second-hand materials as substandard among others. The study categorized the identified barriers for better understanding into six different groups: cultural barriers, social barriers, environmental barriers, economic barriers, technical barriers and technological barriers. Strategies to address the barriers were also proposed. The interrelationships among the various barriers were also shown in a proposed framework to educate professionals on the interconnectivity of the barriers.

Categorization of the various barriers impeding CE uptake contributes to the body of knowledge. Also, the interrelationships among the various categorized barriers in the framework will enable construction professionals make informed decisions regarding the successful integration of CE in the industry, better appreciate the barriers that impede CE uptake and apply strategies to holistically address the barriers. This will expand current knowledge outside the narrow scope of isolated barriers.

To the global construction industry, the review presents a list of barriers and their interrelationships that could provide implementation strategies for the uptake of CE in the industry.

The geographical scope of this study is not limited, and therefore encourages wide applicability of the findings to the global construction industry.