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The key purpose of this study is to systematically examine the landscape of education research funded by the National Plan of Educational Research Funding (NPERF) in China. The study aims to: (1) identify the thematic focus areas that reflect the national education agenda, (2) analyze the general funding patterns of education research projects and (3) gain insights into the distinctive nature of the research agenda in China. The study employs a rigorous data-driven approach to offer valuable insights into the dynamic discourses within the field of education research in China, which has received relatively little attention despite its potential significance.

In this study, we utilized word co-occurrence analysis and corpus-based frequency analysis to analyze the research projects funded by the National Plan of Educational Research Funding (NPERF) from 2011 to 2020.

The key characteristics of these projects highlight the focus on higher education research, addressing the interests of specific cohorts of students, teachers and disadvantaged populations. Furthermore, these projects demonstrate a remarkable responsiveness to the policy needs of the country and a robust inclination toward an international comparative framework.

The findings offer valuable insights into the landscape and features of funded education research in China, revealing a strong emphasis on addressing practical needs and enhancing the capacity of the education system in the country.

This paper presents a systematic examination of the topics covered in funded research under the National Plan of Educational Research Funding (NPERF) scheme from 2011 to 2020. It contributes to the advancement of understanding regarding knowledge traditions and practices in the Chinese context. Methodologically, this paper is the first in the literature to be prototyped with a word co-occurrence analysis approach to systematically investigate the funded education research in China. Additionally, it includes the development of a comprehensive corpus list to uncover the key characteristics of the funded projects. The analysis provides unique insights into the priorities and directions of education research supported by the Chinese government, which are of potential interest to international readers.

While efficient design in engineering projects is crucial, this paper aims to examine the integration of Building Information Modeling (BIM) into railway Intelligent Transportation Systems (ITS). The paper provides some key understanding of integrating BIM and ITS to improve the efficiency of railway infrastructure projects.

An in-depth qualitative analysis of three ITS case studies was conducted to understand BIM’s global impact and benefits in railway infrastructure projects. While case study one investigated the Crossrail (UK), the other two case studies were TUC Rail (Belgium) and the Intercity railway network (Norway).

The findings include the specific benefits of BIM, regarding the railway infrastructure. The result indicated that BIM benefits were consistently the same across all case studies. Although Case study 1 was the only one that boasts a high reduction in waste and reworks, all of the case studies showed less rework and delays due to BIM. The results indicated that the advantages of BIM for such projects are cost optimization, reduction in waste, rework and lessening delays. Subsequently, this leads to the ease and efficiency with which structures and railways can be built. The outcomes can ultimately assist transportation planners in better planning and managing railway projects.

This study proposes the integration of BIM into railway projects as a part of their ITS. The BIM integration into railway projects as a part of their ITS fits within the overall planning to handover phases. Specifically, the BIM integration improves the design process of typical railway projects. Thus, the most significant advantage of BIM for railway projects is to further improve their design process leading to a higher degree of constructability.

Railway infrastructure performs a major role in economic and regional development. The complexity of railway projects continues to increase as the need for more railway infrastructure is on the rise globally. BIM is proving to be an effective tool for improving the efficiency of railway infrastructure projects. As the utilization of BIM is intensifying, the railway industry can further exploit BIM to improve project delivery adeptness by offering greater collaboration leading to efficient design processes. As a result, the understanding of BIM for horizontal projects such as railway infrastructure on a global scale is a substantial exercise that this research aims to respond to.

In the era of the digital economy, higher demands are placed on versatile talents, and the cultivation of students with innovative and entrepreneurial abilities has become an important issue for the further development of higher education, thus leading to extensive and in-depth research by many scholars. The study summarizes the characteristics and patterns of dual-innovation education at different stages of development, hoping to provide a systematic model for the development of dual-innovation education in China and make up for the shortcomings.

This paper uses Citespace software to visualize and analyze the relevant literature in CNKI and Web of Science databases from a bibliometric perspective, focusing on quantitative analysis in terms of article trends, topic clustering, keyword co-linear networks and topic time evolution, etc., to summarize and sort out the development of innovation and entrepreneurship education research at home and abroad.

The study found that the external characteristics of the literature published in the field of bi-innovation education in China and abroad are slightly different, mainly in that foreign publishers are more closely connected and have formed a more stable ecosystem. In terms of research hotspots, China is still in a critical period of reforming its curriculum and teaching model, and research on the integration of specialization and creative education is in full swing, while foreign countries focus more on the cultivation of students' entrepreneurial awareness and the enhancement of individual effectiveness. In terms of cutting-edge analysis, the main research directions in China are “creative education”, “new engineering”, “integration of industry and education” and “rural revitalization”.

Innovation and entrepreneurship education in China is still in its infancy, and most of the studies lack an overall overview and comparison of foreign studies. Based on the econometric analysis of domestic and foreign literature, this paper proposes a path for domestic innovation and entrepreneurship education reform that can make China's future education reform more effective.

This paper aims to enhance lubrication performance of the pitcher plant–like textured surface with various parameters.

A pitcher plant–like structure surface is fabricated on the copper alloy, and the lubrication performance of the pitcher plant–like structure with various parameters is evaluated. In addition, the pressure distribution and oil film load capacity of the pitcher plant–like surface are simulated based on Navier–Stokes equations.

When the direction of motion aligns with the pitcher plant–like structure, the friction coefficient remains lower than that of the nontextured surface, and it exhibits a decreasing trend with the increasing of the texture width and spacing distance; the lowest friction coefficient (0.04) is achieved with B = 0.3 mm, L = 1.0 mm and θ = 45°, marking a 75% reduction compared to the nontextured surface. Simulation results demonstrate that with the increase in texture width and spacing distance, the oil film load-bearing capacity demonstrates an increasing trend.

Bionic pitcher plants are prepared on the copper alloy to improve the lubrication performance and wear resistance.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-04-2024-0119/

This study aims to streamline and enhance the assembly defect inspection process in diesel engine production. Traditional manual inspection methods are labor-intensive and time-consuming because of the complex structures of the engines and the noisy workshop environment. This study’s robotic system aims to alleviate these challenges by automating the inspection process and enabling easy remote inspection, thereby freeing workers from heavy fieldwork.

This study’s system uses a robotic arm to traverse and capture images of key components of the engine. This study uses anomaly detection algorithms to automatically identify defects in the captured images. Additionally, this system is enhanced by digital twin technology, which provides inspectors with various tools to designate components of interest in the engine and assist in defect checking and annotation. This integration facilitates smooth transitions from manual to automatic inspection within a short period.

Through evaluations and user studies conducted over a relatively long period, the authors found that the system accelerates and improves the accuracy of engine inspections. The results indicate that the system significantly enhances the efficiency of production processes for manufacturers.

The system represents a novel approach to engine inspection, leveraging robotic technology and digital twin enhancements to address the limitations of traditional manual inspection methods. By automating and enhancing the inspection process, the system offers manufacturers the opportunity to improve production efficiency and ensure the quality of diesel engines.

Referred data set produces reliable information about the network flows and common attacks meeting with real-world criteria. Accordingly, this study aims to focus on the use of imbalanced intrusion detection benchmark knowledge discovery in database (KDD) data set. KDD data set is most preferably used by many researchers for experimentation and analysis. The proposed algorithm improvised random forest classification with error tuning factors (IRFCETF) deals with experimentation on KDD data set and evaluates the performance of a complete set of network traffic features through IRFCETF.

In the current era of applications, the attention of researchers is immersed by a diverse number of existing time applications that deals with imbalanced data classification (ImDC). Real-time application areas, artificial intelligence (AI), Industrial Internet of Things (IIoT), etc. are dealing ImDC undergo with diverted classification performance due to skewed data distribution (SkDD). There are numerous application areas that deal with SkDD. Many of the data applications in AI and IIoT face the diverted data classification rate in SkDD. In recent advancements, there is an exponential expansion in the volume of computer network data and related application developments. Intrusion detection is one of the demanding applications of ImDC. The proposed study focusses on imbalanced intrusion benchmark data set, KDD data set and other benchmark data set with the proposed IRFCETF approach. IRFCETF justifies the enriched classification performance on imbalanced data set over the existing approach. The purpose of this work is to review imbalanced data applications in numerous application areas including AI and IIoT and tuning the performance with respect to principal component analysis. This study also focusses on the out-of-bag error performance-tuning factor.

Experimental results on KDD data set shows that proposed algorithm gives enriched performance. For referred intrusion detection data set, IRFCETF classification accuracy is 99.57% and error rate is 0.43%.

This research work extended for further improvements in classification techniques with multiple correspondence analysis (MCA); hierarchical MCA can be focussed with the use of classification models for wide range of skewed data sets.

The metrics enhancement is measurable and helpful in dealing with intrusion detection systems–related imbalanced applications in current application domains such as security, AI and IIoT digitization. Analytical results show improvised metrics of the proposed approach than other traditional machine learning algorithms. Thus, error-tuning parameter creates a measurable impact on classification accuracy is justified with the proposed IRFCETF.

Proposed algorithm is useful in numerous IIoT applications such as health care, machinery automation etc.

This research work addressed classification metric enhancement approach IRFCETF. The proposed method yields a test set categorization for each case with error reduction mechanism.