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In the brain imaging based on electrical impedance tomography, it is sometimes not able to attach 16 electrodes due to space restriction caused by craniotomy. As a result of this, the number of boundary measurements decreases, and spatial resolution of reconstructed conductivity distribution is reduced. The purpose of this study is to enhance reconstruction quality in cases of limited measurement.

A new data expansion method based on the shallow convolutional neural network is proposed. An eight-electrode model is built from which fewer boundary measurements can be obtained. To improve the imaging quality, shallow convolutional neural network is constructed which maps limited voltage data of the 8-electrode model to expanded voltage data of a quasi-16-electrode model. The predicted data is compared with the quasi-16-electrode data. Besides, image reconstruction based on L1 regularization method is conducted.

The results show that the predicted data generally coincides with the quasi-16-electrode data. It is found that images reconstructed with the data of eight-electrode model are the poorest. Nevertheless, imaging results when the limited data is expanded by the proposed method show large improvement, and there is a minor difference with the images recovered with the quasi-16-electrode data. Also, the impact of noise is studied, which shows that the proposed method is robust to noise.

To enhance reconstruction quality in the case of limited measurement, a new data expansion method based on the shallow convolutional neural network is proposed. Both simulation work and phantom experiments have demonstrated that high-quality images of cerebral hemorrhage and cerebral ischemia can be obtained when the limited measurement is expanded by the proposed method.

This study aims to comprehensively examine the transitions in household livelihood strategies within rural China, including the underlying processes, drivers and outcomes.

This study uses two waves (2010 and 2018) of longitudinal data from the China Family Panel Studies (CFPS), employing latent cluster analysis, regression models and cumulative distribution function within a dynamic household livelihood strategy framework.

The results show that (1) households’ livelihood strategies can be categorized into four distinct types, i.e. agricultural dominated, agricultural dominated with non-agricultural supplementation, non-agricultural dominated with agricultural supplementation and employment oriented. (2) During 2010–2018, approximately 60% of households underwent transitions in their livelihoods, encompassing both upward and downward trajectories, with a prevalence of upward transitions. (3) Various forms of livelihood capital significantly contribute to upward transitions, while the availability of land resources and exposure to shocks impede the potential for upward mobility. (4) The transition towards non-agricultural livelihood strategies by households leads to a notable enhancement in their livelihood welfare.

In the context of urbanization, industrialization and globalization, rural areas in China are undergoing a gradual socioeconomic transformation, which has also led to changes in rural households’ livelihood strategies. Nevertheless, a dearth of empirical investigation exists regarding the dynamics of rural households’ livelihood strategies, the determinants behind such transitions and the resulting outcomes on their livelihoods. A comprehensive understanding of livelihood transitions can provide valuable insights for policymakers in their endeavors to promote rural revitalization in China.

Based on the nationwide representative datasets in China, it examines the micro-level livelihood transitions of rural households within the broader context of socioeconomic transformation that presents both opportunities and challenges, as well as vulnerable contexts, shaped by various government policies. This exploration would offer valuable theoretical and empirical evidence to advance our understanding of the process, driver and outcome of rural households’ livelihood transition in developing countries.

This work conducts a comprehensive analysis of how to incorporate resilience and sustainability into capacity expansion strategies for business-to-business (B2B) chemical supply chains. This study aims to guide both researchers and managers on ensuring profitability in B2B chemical supply chains while minimizing environmental impacts, complying with regulations and mitigating disruptions and risks.

A systematic literature review is conducted to analyze the interplay between sustainability and resilience in chemical B2B supply chains, specify the quantitative and qualitative methods used to tackle this challenge and identify the drivers and barriers concerning capacity expansion. In addition, a comprehensive conceptual framework is suggested to outline a compelling research agenda.

The findings emphasize the increasing importance of modeling and resolving decision-making challenges related to sustainable and resilient supply chains, particularly in capital-intensive chemical industries. Yet, there is no standardized strategy for addressing these challenges. The predominant solution methods are heuristic and metaheuristic, and the selection of performance metrics tends to be empirical and tailored to specific cases. The main barriers to achieving sustainability and resilience arise from resource limitations within the supply chain. Conversely, the key drivers of performance focus on enhancing efficiency, competitiveness, cost effectiveness and risk management.

This work offers practitioners a conceptual framework that synthesizes the knowledge and tackles the challenges of designing sustainable and resilient supply chains as well as managing their operations in the context of B2B chemical supply chains. Results provide a practical guide for navigating the complex interplay of sustainability, resilience and chemical supply chain expansion.

The key concepts and dimensions associated with capacity expansion planning for a resilient and sustainable chemical supply chain are identified through structured and comprehensive analyses of existing literature. A conceptual framework is proposed for delineating the intersections among sustainability, resilience and chemical supply chain expansions. This mapping endeavor aims to facilitate a future characterized by the deployment of a nexus of resilience and sustainability in chemical supply chains. To this end, a promising future research agenda is accordingly outlined.

With the continuous increase in Web services, efficient identification of Web services that meet developers’ needs and understanding their relationships remains a challenge. Previous research has improved recommendation effectiveness by using correlations between Web services through graph neural networks (GNNs), while it has not fully leveraged service descriptions, limiting the depth and diversity of learning. To this end, a Web services recommendation method called LLMSARec, based on Large Language Model and semantic alignment, is proposed. This study aims to extract potential semantic information from services and learn deeper relationships between services.

This method consists of two core modules: profile generation and maximizing mutual information. The profile generation module uses LLM to analyze the descriptions of services, infer and construct service profiles. Concurrently, it uses LLM as text encoders to encode inferred service profiles for enhanced service representation learning. The maximizing mutual information model aims to align the semantic features of the services text inferred by LLM with structural semantic features of the services captured by GNNs, thus achieving a more comprehensive representation of services. The aligned representation serves as an input for the model to identify services with superior matching accuracy, thereby enhancing the service recommendation capability.

Experimental comparisons and analyses were conducted on the Programmable Web platform data set, and the results demonstrated that the effectiveness of Web service recommendations can be significantly improved by using LLMSARec.

In this study, the authors propose a Web service recommendation approach based on Large Language Model and semantic alignment. By extracting latent semantic information from services and effectively aligning semantic features with structural features, new representations can be generated to significantly enhance recommendation accuracy.

Green innovation and green acquisition are key green marketing strategies. This paper aims to explore and compare the drivers of green acquisition and green innovation strategies firms adopt. Moreover, the moderating role of top management team (TMT) sustainability commitment is investigated.

The research model used secondary data based on 1,565 firm-year observations in the beverage and food industry in the US. The two-stage control function approach was used for data analysis.

Media attention motivates firms to pursue both green innovation and green acquisition. The TMT sustainability commitment plays a pivotal moderating role. It strengthens the link between environmental regulation stringency and green innovation but weakens the impact of media attention on green acquisition.

Managers can leverage the study’s findings to guide sustainable marketing decisions in response to environmental regulations and media scrutiny. Policymakers and investors can encourage firms to adopt more sustainable practices, helping align corporate strategies with Sustainable Development Goals 9 and 12.

Though green innovation determinants are extensively studied, most studies rely on surveys or qualitative methods rather than secondary data. Also, as an alternative to developing in-house green technologies or products, the drivers of green acquisition remain unclear despite its growing prevalence. This study addresses both gaps in the sustainable marketing literature.

Organic coatings are widely used for protecting metal equipment and structures from corrosion. Accurate detection and evaluation of the protective performance and service life of coatings are of great importance. This paper aims to review the research progress on performance evaluation and lifetime prediction of organic coatings.

First, the failure forms and aging testing methods of organic coatings are briefly introduced. Then, the technical status and the progress in the detection and evaluation of coating protective performance and the prediction of service life are mainly reviewed.

There are some key challenges and difficulties in this field, which are described in the end.

The progress is summarized from a variety of technical perspectives. Performance evaluation and lifetime prediction include both single-parameter and multi-parameter methods.

This paper analyzes the application of digital twin technology in the field of intelligent operation and maintenance of high-speed railway infrastructure from the perspective of top-level design.

This paper provides a comprehensive overview of the definition, connotations, characteristics and key technologies of digital twin technology. It also conducts a thorough analysis of the current state of digital twin applications, with a particular focus on the overall requirements for intelligent operation and maintenance of high-speed railway infrastructure. Using the Jinan Yellow River Bridge on the Beijing–Shanghai high-speed railway as a case study, the paper details the construction process of the twin system from the perspectives of system architecture, theoretical definition, model construction and platform design.

Digital twin technology can play an important role in the whole life cycle management, fault prediction and condition monitoring in the field of high-speed rail operation and maintenance. Digital twin technology is of great significance to improve the intelligent level of high-speed railway operation and management.

This paper systematically summarizes the main components of digital twin railway. The general framework of the digital twin bridge is given, and its application in the field of intelligent operation and maintenance is prospected.

The global COVID-19 epidemic has posed significant challenges to the development of innovation and entrepreneurship education in Chinese and foreign universities, and the application of artificial intelligence generated content (AIGC) technology has presented both opportunities and challenges to its development. The purpose of this study is to summarize advanced experiences and models of new engineering innovation and entrepreneurship education development in Chinese and foreign universities, as well as to analyze the influencing factors. Taking the sustainable development goals (SDGs) into account, this study qualitatively proposes enhancement paths and improvement suggestions based on the application of AIGC technology, providing a reference for promoting the sustainable development (SD) of innovation and entrepreneurship education in Chinese universities.

By using the qualitative comparative analysis (QCA), this paper studies the interaction mechanism between the influencing factors of innovation and entrepreneurship in universities under the background of SDGs. This paper selects 12 representative universities with different cultures and strengths. Meanwhile, this paper analyzes the content of 2,535 publications on innovation and entrepreneurship education and summarizes seven influencing factors as comparison criteria. Then, this paper codes, summarizes and uses configuration to assess the primary factors influencing the development of innovation and entrepreneurship in colleges and universities at home and abroad.

On the quality of new engineering innovation and entrepreneurship education, comprehensive, diverse influencing factors and upgrading paths are obtained. Furthermore, this research proposes that the SD of innovation and entrepreneurship education in universities should make effective use of “AI plus education” and actively construct practical and teaching platforms. Meanwhile, the ChatGPT is being used to strengthen the innovation and entrepreneurship curricular system and talent training mode. The research also makes recommendations for improving teachers’ ability to acquire intelligent tools and promotes three-way teaching modalities of “teacher-AI-student” by taking into account the influence of various aspects.

This research uses the QCA research method, which analyzes not only influencing factors on the SD of innovation and entrepreneurship education but also explores the interaction mechanisms among factors. Furthermore, the research incorporates SDGs and AIGC technology application scenarios into the field of domestic innovation and entrepreneurship education, which will be helpful in SDGs of innovation and entrepreneurship education on both theoretical and practical levels.

This study investigates the impact of perceived ease of use on mobile shoppers’ inclination to make impulsive purchases in the context of mobile commerce. The authors employ a technology acceptance model to examine how perceived ease of use affects the impulsive tendencies of smartphone users. Additionally, we utilise a motivation orientation framework to understand the moderating effect of utilitarian and hedonic values in the relationship between perceived ease of use and impulsive buying behaviour.

A self-administered online survey was conducted on 427 users of mobile commerce applications. Structural equation modelling was employed through SPSS AMOS 26 to analyse the collected data.

According to the findings, perceived ease of use has a substantial negative connection with impulsive buying behaviour. This inverse relationship is interesting as it shows that user-friendliness alone may not be enough to drive spontaneous buying. However, hedonic and utilitarian values moderated the connection between perceived ease of use and impulsive buying behaviour.

The study’s findings will help marketers, policymakers, organisations and academicians comprehend the significance of hedonic and utilitarian values in driving IBB and making rational decisions.

This study contributes to the technology acceptance model and motivation orientations by determining the crucial role of utilitarian-hedonic values in triggering impulsive purchases in the mobile commerce sector of a non-tech savvy developing country.

The contractors’ consummate performance behavior is the key to achieving the project’s value added, but existing research has paid little attention to how to stimulate this behavior. Based on contractual functions and regulatory focus theory, this study examined how the allocation of contractual functions and the contractors’ regulatory focus affect their consummate performance behaviors. At the same time, considering the important position of guanxi between owner and contractor, this study also examined the moderating effect of guanxi on the relationship between the contractors’ regulatory focus and consummate performance behaviors.

This study first constructs a conceptual model that incorporates contractual functions (control, coordination and adaptation), contractors’ regulatory focus (promotion focus, prevention focus) and the effect of guanxi on contractors’ consummate performance behavior. Next, partial least squares structural equation modeling is used to analyze the survey data of 297 Chinese construction project professionals.

This study has the following four findings. First, contractual control has a negative effect on contractors’ promotion focus but a positive effect on their prevention focus. Contractual coordination and adaptation have a positive effect on contractors’ promotion focus but a negative effect on their prevention focus. Second, contractors’ promotion focus has a positive effect on their consummate performance behaviors, while their prevention focus has a negative effect on such behaviors. Third, both of contractors’ promotion focus and prevention focus play a mediating role in the relationship between contractual functions and their consummate performance behaviors. Finally, guanxi plays a moderating role in the relationship between contractors’ regulatory focus and their consummate performance behaviors.

Theoretically, this study enriches the research on the antecedents of contractors’ regulatory focus and extends the literature on contractual and guanxi management in construction projects. In practice, this study can provide guidance for improving contractors’ consummate performance behaviors and reasonable allocation of contractual functions.