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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 paper aims to explore blockchain integration strategies within a three-level livestock meat supply chain in which consumers have a preference for quality trust in livestock meat products. The paper investigates three questions: First, how does consumers’ preference for quality trust affect blockchain integration and transaction decisions among supply chain participants? Second, under what circumstances will retailers choose to participate in the blockchain? Finally, how can other factors such as blockchain costs and supplier–retailer partnership value affect integration decisions?

This paper formulates a supply chain network equilibrium model and employs the logarithmic-quadratic proximal prediction-correction method to obtain equilibrium decisions. Extensive numerical studies are conducted using a pork supply chain network to analyze the implications of blockchain integration for different supply chain participants.

The results reveal several key insights: First, suppliers’ increased blockchain integration, driven by higher quality trust preference, can negatively affect their profits, particularly, with excessive trust preferences and high blockchain costs. Second, an increase in consumers’ preference for quality trust expands the range of unit operating costs for retailers engaging in blockchain. Finally, the supplier–retailer partnership drives retailer blockchain participation, facilitating enhanced information sharing to benefit the entire supply chain.

This study provides original insights into blockchain integration strategies in an agricultural supply chain through the application of the supply chain network equilibrium model. The investigation of several key factors on equilibrium decisions provides important managerial implications for different supply chain participants to address consumers’ preference for quality trust and enhance overall supply chain performance.

Social media has progressively upgraded an interactive domain via online sociability and information-sharing. This study aims to formulate an information-sharing intention model by identifying the decisive role of intrinsic and extrinsic motivations.

Empirical data from 508 participants were collected to examine the structural model using structural equation modeling.

Results indicate that information-sharing intention is strongly promoted by intrinsic and extrinsic motivations. Furthermore, perceived herding, perceived crowd and intrinsic motivation boost substantially extrinsic motivation. Perceived herding is of utmost importance to extrinsic motivation, whereas emotional appeal and informative appeal are of paramount importance to intrinsic motivation. Moreover, source trust and exhibitionism are underlying motivations for intrinsic motivation.

The findings provide useful guidelines for practitioners to urge users into information-sharing via social media.

This study contributes significantly to the current literature by developing an effective mechanism of information-sharing through social media based on the motivational theory.

Social commerce (s-commerce) has gained widespread popularity as a social platform where customers engage in resource-sharing activities such as information exchange, advice-seeking and expressing their opinions on mutual interests. However, existing studies have not fully comprehended the drivers of electronic customer-to-customer interaction (eCCI) and how such behavior contributes to the customer “stick” on s-commerce sites. This study develops the Motivation–Opportunity–Ability (MOA) theory and investigates the impact of MOA factors on eCCI, which in turn affects customer stickiness.

A survey was used to acquire data from 455 valid respondents, and the research employed a combination of fuzzy-set qualitative comparative analysis (fsQCA) and structural equation modeling.

The results revealed associations between perceived self-efficacy, intrinsic motivation, tie strength with other customers, eCCI and customer stickiness.

Considering the limited availability of complete eCCI frameworks in existing scholarly works, the authors present valuable perspectives on the role of consumer characteristics as both antecedents and consequences of eCCI. Additionally, this study proposes a research agenda for the field of eCCI on s-commerce sites.

The implementation of the digital economy has had a considerable influence on the manufacturing industry, and this paper aims to address the important issues of how to capture the opportunities presented by digital innovation and promote the transformation and upgrading of the manufacturing industry, as well as the improvement of quality and efficiency.

Using panel data from 30 Chinese provinces and cities between 2010 and 2021, this study establishes the panel vector autoregression (PVAR) model and uses impulse response function analysis to evaluate the influence of the digital economy on the high-quality transformation and upgrading of China's small home appliance industry across five dimensions under the digital economy.

The development of digital infrastructure has not demonstrated a noteworthy capacity for advancing the transformation and upgrading of the small home appliance industry. Furthermore, digital industrialization has exerted a minimal restraining influence on this process. Nevertheless, digital governance has consistently exhibited a substantial impact on facilitating the transformation and upgrading of the small home appliance industry. While both industrial digitization and digital innovation hold significant potential for promoting the transformation and upgrading of the small home appliance industry, their sustainability remains limited.

The organization should logically join independent innovation and open innovation, construct an industrial ecosystem for the profound convergence of the digital economy and compact household appliances, use digital-wise science and technology to empower the establishment of brand effects, strengthen the portrayal of the digital standard framework for the intelligent compact household appliance industry, advance the development of a public stage for computerized administrations in the compact household appliance industry and develop a strategy ecosystem for computerized assets in the compact household appliance industry.

This study offers systematic evidence of the relationship between the digital economy and the development of the small home appliance industry. The results of this research contribute to the literature on the impact of the digital economy on the manufacturing sector and provide a logical explanation for the transformation and upgrading of the small home appliance industry within the context of the digital economy.

This study aims to design and implement a multimaterial system for printing multifunctional specimens suitable for various sectors, with a particular focus on biomedical applications such as addressing mandibular bone loss.

To enhance both the mechanical and biological properties of scaffolds, an automatic multimaterial setup using vat photopolymerization was developed. This setup features a linear system with two resin vats and one ultrasonic cleaning tank, facilitating the integration of diverse materials and structures to optimize scaffold composition. Such versatility allows for the simultaneous achievement of various characteristics in scaffold design.

The printed multimaterial scaffolds, featuring 20 Wt.% hydroxylapatite (HA) on the interior and poly-L-lactic acid (PLLA) with 1 Wt.% graphene oxide (GO) on the exterior, exhibited favorable mechanical and biological properties at the optimum postcuring and heat-treatment time. Using an edited triply periodic minimal surface (TPMS) lattice structure further enhanced these properties. Various multimaterial specimens were successfully printed and evaluated, showcasing the capability of the setup to ensure functionality, cleanliness and adequate interface bonding. Additionally, a novel Gyroid TPMS scaffold with a nominal porosity of 50% was developed and experimentally validated.

This study demonstrates the successful fabrication of multimaterial components with minimal contaminations and suitable mechanical and biological properties. By combining PLLA-HA and PLLA-GO, this innovative technique holds significant promise for enhancing the effectiveness of regenerative procedures, particularly in the realm of dentistry.

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.