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This study aims to provide a reference basis for waterproofing for the long-term safe operation of shield tunnels. Shielding subways in the long-term operation of tunnel tube seams leads to opening, dislocation and other issues, which in turn cause the tube sealing gasket to break and ultimately cause water seepage, and the existing symmetrical sealing gasket arrangement cannot meet the waterproofing requirements of the tunnel structure.

First, we carry out an indoor “one-seam” hydrostatic test to quantitatively determine the waterproofing performance of symmetric and four asymmetric arrangements of gaskets. And the arrangement with the best stability and waterproofing performance is selected. Second, we establish a three-dimensional numerical seepage model for the waterproof failure of gaskets with different arrangements, which mechanistically explains the whole course of the gradual failure of the waterproof performance of gaskets with the wedging of water. Finally, we compare and analyze the experimental results with the numerical results to verify the reliability of the different analysis methods.

The results of the research show that the gasket will undergo four stages: the initial stage, deformation stage, wedging stage, and breakthrough stage during the continuous wedging process of the water body. Compared with the symmetric arrangement of the gasket, the asymmetric arrangement of the effective contact part of the gasket stress wave peaks and troughs is smaller, the deformation stage of the ability to resist the deformation of the water pressure is stronger, and the role of the water pressure between the two sealing gaskets of the stress path is less likely to be damaged.

The current test can't fully reproduce real engineering site conditions as it ignores factors like temperature, time and aging during waterproofing tests and lacks tests based on actual application. Only one – seam test is done, lacking research on other seams. The current seepage model has difficulty reflecting some details and needs refinement.

The study focuses on the tube sheet joint problem in underground tunnels and proposes four asymmetric gasket arrangements, which are tested and analysed using a variety of methods. The results show that the asymmetric arrangement has a slower decline in waterproofing capacity and better stability, providing a new method and basis for solving tunnel waterproofing problems.

The study focuses on the tube sheet joint problem in underground tunnels and proposes four asymmetric gasket arrangements, which are tested and analysed using a variety of methods. The results show that the asymmetric arrangement has a slower decline in waterproofing capacity and better stability, providing a new method and basis for solving tunnel waterproofing problems.

This study aims to shape the future trajectory of scholarly research on traditional, reputational and societal supply chain risks and their management.

The research uses a narrative literature review of the overview type. To control bias stemming from the subjectivity of the methodology, the authors synthesized the relevant literature transparently and established various safeguarding procedures.

The established research stream on traditional supply chain risk has generated a wealth of concepts that can potentially be transferred to the study of reputational and societal risks. The maturing research stream on reputational risks has mostly focused on risk manifestation, from the upstream perspective of the focal firm. The emerging scholarship on societal supply chain risks has anecdotally highlighted detrimental effects on contextual actors, such as society-at-large.

This study shifts scholarly attention to the role of the context in the risk manifestation process – as a potential risk source for traditional supply chain risk, during the risk materialization for reputational supply chain risk, and as the locus of the risk effect for societal supply chain risk.

This review is unique in that it fosters a holistic understanding of supply chain risk and underscores the increased importance of the context for it. The socioeconomic, institutional and ecological contexts connect the three reviewed research streams. Detailed research agendas for each literature stream are developed, comprising 23 topical areas in total.

With the development of data mining technology, diverse and broader domain knowledge can be extracted automatically. However, the research on applying knowledge mapping and data visualization techniques to genealogical data is limited. This paper aims to fill this research gap by providing a systematic framework and process guidance for practitioners seeking to uncover hidden knowledge from genealogy.

Based on a literature review of genealogy's current knowledge reasoning research, the authors constructed an integrated framework for knowledge inference and visualization application using a knowledge graph. Additionally, the authors applied this framework in a case study using “Manchu Clan Genealogy” as the data source.

The case study shows that the proposed framework can effectively decompose and reconstruct genealogy. It demonstrates the reasoning, discovery, and web visualization application process of implicit information in genealogy. It enhances the effective utilization of Manchu genealogy resources by highlighting the intricate relationships among people, places, and time entities.

This study proposed a framework for genealogy knowledge reasoning and visual analysis utilizing a knowledge graph, including five dimensions: the target layer, the resource layer, the data layer, the inference layer, and the application layer. It helps to gather the scattered genealogy information and establish a data network with semantic correlations while establishing reasoning rules to enable inference discovery and visualization of hidden relationships.

This papers aims to study lattice structures in terms of geometric variables, manufacturing variables and material-based variants and their correlation with compressive behaviour for their application in a methodology for the design and development of personalized elastic therapeutic products.

Lattice samples were designed and manufactured using extrusion-based additive manufacturing technologies. Mechanical tests were carried out on lattice samples for elasticity characterization purposes. The relationships between sample stiffness and key geometric and manufacturing variables were subsequently used in the case study on the design of a pressure cushion model for validation purposes. Differentiated areas were established according to patient’s pressure map to subsequently make a correlation between the patient’s pressure needs and lattice samples stiffness.

A substantial and wide variation in lattice compressive behaviour was found depending on the key study variables. The proposed methodology made it possible to efficiently identify and adjust the pressure of the different areas of the product to adapt them to the elastic needs of the patient. In this sense, the characterization lattice samples turned out to provide an effective and flexible response to the pressure requirements.

This study provides a generalized foundation of lattice structural design and adjustable stiffness in application of pressure cushions, which can be equally applied to other designs with similar purposes. The relevance and contribution of this work lie in the proposed methodology for the design of personalized therapeutic products based on the use of individual lattice structures that function as independent customizable cells.

This study aims to clarify influencing factors of overcapacity of new energy enterprises in China and accurately predict whether these enterprises have overcapacity.

Based on relevant data including the experience and evidence from the capital market in China, the research establishes a generic univariate selection-comparative machine learning model to study relevant factors that affect overcapacity of new energy enterprises from five dimensions. These include the governmental intervention, market demand, corporate finance, corporate governance and corporate decision. Moreover, the bridging approach is used to strengthen findings from quantitative studies via the results from qualitative studies.

The authors' results show that the overcapacity of new energy enterprises in China is brought out by the combined effect of governmental intervention corporate governance and corporate decision. Governmental interventions increase the overcapacity risk of new energy enterprises mainly by distorting investment behaviors of enterprises. Corporate decision and corporate governance factors affect the overcapacity mainly by regulating the degree of overconfidence of the management team and the agency cost. Among the eight comparable integrated models, generic univariate selection-bagging exhibits the optimal comprehensive generalization performance and its area under the receiver operating characteristic curve Area under curve (AUC) accuracy precision and recall are 0.719, 0.960, 0.975 and 0.983, respectively.

The proposed integrated model analyzes causes and predicts presence of overcapacity of new energy enterprises to help governments to formulate appropriate strategies to deal with overcapacity and new energy enterprises to optimize resource allocation. Ten main features which affect the overcapacity of new energy enterprises in China are identified through generic univariate selection model. Through the bridging approach, the impact of the main features on the overcapacity of new energy enterprises and the mechanism of the influence are analyzed.

The rapid increase in importance of the remanufacturing operation in the present scenario is just because of its ability to retrieve the functional value of the End-of-Use or End-of-Life products which is as good as the original product. However, customers are still concerned about the reliability of the remanufactured product which is considered as one of the major problems in the area of remanufacturing. The purpose of this paper is to study and analyse the behavioural pattern of the mixture failure rate of a remanufactured product.

In order to analyse the behavioural pattern of the mixture failure rate, different proportions of new and remanufactured products are mixed. In this paper, a two-parameter Weibull distribution is used to observe the mixture failure rate characteristics. Also, the mixture failure rate of the remanufactured product is evaluated under two conditions, that is when the shape parameter of new and remanufactured components is the same and when the shape parameter values are different.

From the analysis, it is observed that the mixture failure rate is always decreasing in nature when the shape parameter values are same. In that case, the value of the mixture failure rate depends only on the proportion of the new components. When the shape parameter values are different, the mixture failure rate characteristics depend upon the shape parameter value of the remanufactured product.

The results of the research can be applied to any remanufactured automotive product. This study also shows the behavioural characteristics of the mixture failure rate of a remanufactured product at different mixture proportions.

This study aims to investigate the impact of brain drain on government AI readiness in EU member countries, considering the distinctive governance characteristics, macroeconomic conditions and varying levels of ICT specialists.

The research employs a dynamic panel data model using the System Generalized Method of Moments (GMM) to analyze the relationship between brain drain and government AI readiness from 2018 to 2022. The study incorporates various control variables such as GDP per capita growth, government expenditure growth, employed ICT specialists and several governance indicators.

The results indicate that brain drain negatively affects government AI readiness. Additionally, the presence of ICT specialists, robust governance structures and positive macroeconomic indicators such as GDP per capita growth and government expenditure growth positively influence AI readiness.

Major limitations include the focus on a specific region of countries and the relatively short period analyzed. Future research could extend the analysis with more comprehensive datasets and consider additional variables that might influence AI readiness, such as the integration of AI with emerging quantum computing technologies and the impact of governance reforms and international collaborations on AI readiness.

The theoretical value of this study lies in providing a nuanced understanding of how brain drain impacts government AI readiness, emphasizing the critical roles of skilled human capital, effective governance and macroeconomic factors in enhancing AI capabilities, thereby filling a significant gap in the existing literature.

This research fills a significant gap in the existing literature by providing a comprehensive analysis of the interaction between brain drain and government AI readiness. It uses control variables such as ICT specialists, governance structures and macroeconomic factors within the context of the European Union. It offers novel insights for policymakers to enhance AI readiness through targeted interventions addressing brain drain and fostering a supportive environment for AI innovation.