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As the foundation of social and economic development, infrastructure development projects are characterized by large initial investment, high technical requirements and thus generally delivered through complex contractor–subcontractor collaboration chains. This study aims to characterize the complexity of collaborative networks between contractors and subcontractors for infrastructure development through comparing the structural characteristics and the formation mechanisms of contractor–subcontractor collaborative networks for the following two different types of infrastructure: public works (PWCN) owned and operated by government agencies, and public utilities (PUCN) owned and operated by nongovernment agencies.

Based on the method of stochastic actor-oriented models and the longitudinal dataset of National Quality Award Projects in China during 2001–2020, this study compares how the structural characteristics of project-based collaborative networks between contractors and subcontractors for the two types of projects are different and how related micro-mechanisms, including both structure-based endogenous network effects and attribute-based exogenous homophily effects (institutional, organizational and geographical homophily), collectively underpin the formation of the networks.

The empirical results provide evidence that while the two networks are both characterized by relatively low levels of network density, PWCN is more globally connected around a minority of superconnected contractors as compared with PUCN. The results further reveal that compared with PUCN, the formation of PWCN is more significantly related to the structure-based anti in-isolates effect, suggesting that PWCN is more open for new entrant subcontractors. With regard to the attribute-based homophily effects, the results provide evidence that while both significantly and positively related to the effects of organizational (same company group) and geographical homophily (same location), the formation of PWCN and PUCN is oppositely driven by the institutional homophily effect (same ownership type).

As an exploratory effort of using network perspective to investigate the formation mechanisms of contractor–subcontractor relationships in the infrastructure development domain, this study contributes to a network and self-organizing system view of how contractors select subcontractors in different types of infrastructure projects. The study also provides insights into how contractor–subcontractor collaborative relationships can be better manipulated to promote the development of complex infrastructure in different contexts.

The purpose of this paper is to conduct a thorough study of the current research status and trends of Artificial Intelligence-Generated Content (AIGC), which is thriving and exerting significant influences on society, the economy and technology. This study will encompass both the ample opportunities and the array of emerging risks and challenges associated with AIGC. Furthermore, this paper seeks to propose practical optimization strategies to facilitate its continued development.

A total of 12,702 documents in Scopus, CNKI and Altmetric.com databases are analyzed in this paper. The visualization tools of CiteSpace and Netdraw are used to systematically analyze AIGC from macro, meso and micro perspectives based on bibliometric indicators. The analysis is combined with altmetrics indicators to identify hotspots in AIGC-related research and predict future development trends. Finally, substantive optimization suggestions for the development of AIGC are put forward.

Research has found that firstly, both domestic and international research in the field of AIGC is actively catching up with the pace of the era. Institutions and authors recognize the indispensability of collaboration, leading to the emergence of interdisciplinary cooperation trends. Secondly, at the thematic research level, both domestic and international studies cover the overall trends of AIGC, including technology, applications, challenges and coping strategies. However, international scholars tend to focus more on technological breakthroughs, while domestic researchers emphasize the formulation of national laws and regulations. Finally, through the analysis of hotspots and trends, it is predicted that future research will focus more on addressing the legal issues regarding the originality of AIGC, enhancing its interactivity, optimizing its applications, emphasizing ethical considerations and efficiently addressing major social issues such as pandemics using AIGC technology. Corresponding optimization strategies are proposed to ensure the development of AIGC is consistent with global consensus and values, creating a beneficial environment for its sustainable development.

Firstly, this paper integrates data from three databases and uses multiple software tools to conduct a comparative analysis of the research trends in AIGC from both domestic and international perspectives. Secondly, this paper not only analyzes the academic influence of AIGC-related research through bibliometric indicators but also combines altmetrics indicators to explore the public’s attitude toward AIGC. This method systematically reveals the overall development trends, leading institutions and researchers, thematic research areas, hotspot evolution and future development trends of AIGC-related research, providing theoretical references for subsequent scholars in AIGC research. Additionally, this paper helps governments, institutions and organizations to precisely and wisely formulate policies and investments, as well as to effectively comprehend the development trends of AIGC and promote the advancement of AIGC technology and applications.

To improve the localization accuracy of the magnetically controlled capsule endoscope (MCCE), a new localization method, based on the magnetic dipole model, is proposed, where the anti-disturbance permanent magnet (APM) is used as the source of stable magnetic field, thus reducing the interference of the geomagnetic field or the electric conductor magnetic field in the system.

The coupling magnetic force model between the APM and the capsule endoscope is established to obtain the magnetic force relationship and magnetic induction intensity. Along the three axes, magnetic induction intensity data are collected by a 3 × 3 sensor array composed of nine magnetic field intensity sensors, while the data are uploaded to the main computer by the STM32F103C8T6 control board over a ESP8266 WIFI module connection. Next, the axial magnetic induction intensity data are decoupled to obtain the measurement trajectory, whereas the error function is established based on the calculated trajectory parameters. Finally, the Levenberg–Marquardt (L-M) algorithm is used to solve the position information of the MCCE.

Experiments show that the average localization error of an MCCE in a straight and circular bend tube is 4.76 mm, whereas in a U-bend tube, it is 6.82 mm.

The optimized simulation value in the linear and bending environment is in good agreement with the experimental value, which verifies the accuracy of the MCCE localization system based on magnetic field sensor array, exhibiting good performance in localization and position tracking while providing a theoretical basis for the subsequent research.

This study aims to investigate how small and medium-sized enterprises (SMEs’) supply chain specific investment (SCSIs) affects supply chain financing performance (SCFP) in the innovative industrial finance model, and further analyze the internal mechanisms and important contextual factors.

Based on signaling theory, this study constructs a mediating and moderating model to examine the influencing mechanisms of SMEs’ SCSIs on SCFP, including the mediating effect of opportunism and the moderating effect of digital technology deployment (DTD). A multiple regression analysis is conducted to verify the theoretical hypotheses, using questionnaire data collected from 288 SMEs in China.

The empirical findings indicate that both SMEs’ supply chain asset-specific and relationship-specific investments can significantly promote SCFP. Also, SMEs’ SCSIs can improve SCFP by reducing the occurrence of opportunism perceived by supply chain partners. The breadth of DTD positively moderates the relationship between the two types of SCSIs and SCFP, while the depth of DTD has no significant moderating effect on the relationship between SCSIs and SCFP.

This study has discussed the important and novel issue of how financially distressed SMEs can send effective signals to financial institutions by increasing their SCSIs in supply chain finance mode. By revealing the influencing mechanisms of SMEs’ SCSIs on SCFP, this study contributes to expanding the research on the antecedents of SCFP from the dimension of interorganizational transactions. This study also enriches the perspectives of signaling theory by exploring the interaction between signal sender and signal intermediary.

In a volatile and highly dynamic business environment, supply chain operations face various challenges. This study, grounded in dynamic capability theory (DCT), investigates how supply chain collaboration (SCC) and supply chain transparency (SCT) influence the relationship between supply chain finance (SCF) and supply chain resilience (SCR) in manufacturing firms. This demonstrates the intrinsic mechanism between SCF and SCR, which provides insights for relative research.

The quantitative research approach was utilized. Employing data collected from 289 Chinese manufacturing firms, structural equation modeling is conducted to test the theoretical hypotheses.

The research findings have revealed that SCF has a positive impact on SCC and SCT and SCC has a positive impact on SCT. Moreover, both SCC and SCT can improve SCR, and SCT plays a positive mediating role between SCC and SCR.

This study provides insightful ideas for managers in enhancing SCR by considering SCF. Moreover, this study highlights the concrete responses firms should do to improve SCC and SCT, thus managers can develop more effective strategies to optimize SCR.

This study contributes to the extant literature on improving SCR through SCF, considering the two dimensions of dynamic capability (i.e. SCC and SCT), which explains the intrinsic mechanisms by which SCF affects SCR. This study also broadens the scope of application of DCT.

This paper aims to improve the refractive index sensor performance for analytes with large refractive index by adopting the technology of microstructured fiber (MF) and surface plasmon resonance (SPR).

The structure adopts an MF with a hexagonal lattice cladding structure composed of all-circular air holes, and three defect regions are introduced. The liquid analyte that needs to be tested is filled in the defect area. The surface plasmon polarition mode is generated and coupled with the core mode, thus forming a refractive index sensing channel. When the resonance conditions are satisfied, the resonance wavelength will be changed with the refractive index of the liquid analyte. All parameters that may affect the performance of the sensor are numerical simulated, and the structure is optimized through a large number of calculations.

The results demonstrate that the maximum dynamic sensitivity (S_R) can reach to 24,260 nm/RIU, and the average sensitivity (S_R-AV) can reach to 18,046 nm/RIU when the refractive index range is from 1.42 to 1.47. Besides, the sensitivity linearity (R²) is approximately 0.965, and its resolution is 4.1 × 10^–6 RIU. The comparison with some literature results shown that the proposed sensor has certain advantages over the sensors reported in these literatures.

This work proposed an SPR-based refractive index sensor with a simple MF structure. It has a certain reference significance for the design and optimization of SPR-based MF sensors. Moreover, owing to its simple structure, high refractive index sensitivity and linear sensing performance, this sensor will play an important role in the detection of high refractive index liquid analytes.

As a clean and renewable energy source, wind energy will become one of the main sources of new energy supply in the future. Relying on the stable and strong wind resources at sea, wind energy has great potential to become the primary energy. As a critical part of the wind turbine, the gearbox of a wind turbine often bears a large external load. Especially at sea, due to the effects of ocean corrosion, waves and wind, the burden of the wind turbine gearbox is greater, which brings great challenges to its reliability analysis. This study aims to systematically review the reliability research in wind turbine gearboxes and guide future research directions and challenges.

This study systematically reviews some design requirements and reliability analysis methods for wind turbine gearboxes. Then, it summarizes previous studies on wind load uncertainty modeling methods, including the processing of wind measurement data and the summary of three different classifications of random wind speed prediction models. Finally, existing reliability analysis studies on two major parts of the gearbox are described and summarized.

First, the basic knowledge of wind turbine gearboxes and their reliability analysis is introduced. The requirements and reliability analysis methods of wind turbine gearboxes are explained. Then, the processing methods of wind measurement data and three different random wind speed prediction models are described in detail. Furthermore, existing reliability analysis studies on two common parts of wind turbine gearboxes, gears and bearings, are summarized and classified, including a summary of bearing failure modes. Finally, three possible future research directions for wind turbine gearbox reliability analysis are discussed, namely, reliability research under the influence of multiple factors on gears, damage indicators of bearing failure modes and quantitative evaluation criteria for the overall dynamic characteristics of offshore wind turbine gearboxes and a summary is also given.

This paper aims to systematically introduce the relevant contents of wind turbine gearboxes and their reliability analysis. The contents of wind speed data processing, predictive modeling and reliability analysis of major components are also comprehensively reviewed, including the classification and principle introduction of these contents.

This paper aims to investigate the motivational drivers for knowledge sharing (KS) through the application of mobile social networking (MSN) and their influence on employee productivity in the Malaysian construction industry.

The data was collected by distributing a self-administered questionnaire among employees working in the construction industry. A total of 250 questionnaires were collected and analysed using partial least squares structural equation modeling. This study used a variety of analysis methodologies including correlation analysis, reliability and validity testing, as well as a structural model in evaluating the relationships between KS, motivational factors and influence on productivity.

The empirical results of this study reveal that utilitarian motivation is positively related to internal and external KS. Similarly, internal and external KS have a positive influence on productivity, while internal and external hedonic motivation does not show any significant relationship with productivity.

Considering the paucity of evidence regarding the determinants of KS motivational drives on employee productivity in the Malaysian construction industry, this study enriches the social cognitive theory-based literature on KS.

This study provides some insights to the players in the construction industry in implementing the most appropriate KS promotion strategies that could potentially influence employee productivity. Organisations in the construction industry that place great emphasis on utilitarian motivation including competency, reward and incentives are more likely to have a better influence on KS and, in turn, positively impact employee productivity.

This study meaningfully contributes to enhancing the understanding of the motivational drivers for sharing knowledge on MSN and its influence on productivity. The findings of this study potentially assist construction practitioners in developing a holistic blueprint for managing their KS towards improving the overall productivity of their employees.

Wine is a complicated and difficult product to know, which makes it extremely difficult for people with little knowledge to choose the wine they want. This study aims to analyze whether the vocabulary used in reviews on wine written by experts and amateurs on the specialized website is useful for those consumers who wish to search for information on this website to choose a wine.

The analysis combines text mining, Natural Language Processing and the Biterm Topic model applied to 25,847 reviews, evaluating a total of 13,263 Spanish wines made by 17 selected users of a specialized wine website.

The results show that wine consumers and users of the specialized wine website who write reviews can be divided into expert users and amateur users. Both experts and amateurs use a specific vocabulary related to the wines they review. Unlike amateurs, experts have a broader and more precise vocabulary, and greater consistency in the use of words with the aspects of the wine. For the revised wines, they address fewer and more specific aspects of wine (such as vintages), but they do so with more depth and rigor.

The originality and value of this research work lie in addressing two aspects that have hardly been analyzed: the reviews of experienced consumers and amateur consumers, and the textual information referring to the Spanish language, which distinguishes this analysis from other similar analyses carried out on the English language.

Given the decisive role of knowledge-oriented leadership (KOL) in boosting organizational innovation capacities, the research intends to investigate the effect of KOL on ambidextrous innovation with the mediating effect of knowledge management capability (KMC). Furthermore, technological turbulence (TT) is regarded as a moderator in the relationship between KMC and ambidextrous innovation.

The data obtained from 122 Pakistani manufacturing firms were used to evaluate the proposed relationships using the partial least square structural equation modeling approach.

The empirical findings demonstrate that KOL positively affects both aspects of ambidextrous innovation, namely exploitative innovation (EII) and exploratory innovation (ERI), with a higher effect on EII. Additionally, knowledge management process capability (KMPC) partially mediates the association between KOL and both dimensions of ambidextrous innovation (EII and ERI). Similarly, knowledge management infrastructure capability (KMIC) mediates the link between KOL and ERI but does not mediate the relationship between KOL and EII. The impacts of the KMPC and KMIC on EII are also significantly moderated by TT, although the link between the two components of the KMC (KMPC and KMIC) and ERI is unaffected.

The research provides useful knowledge and a novel strategy for policymakers to foster KOL and invest in KMC to improve the capabilities of Pakistani manufacturing firms in terms of innovation.

The research has contributed significantly to the resources-based view and knowledge-based view (KBV) literature by examining the various mediation moderation mechanisms and offering greater insights into the relationship between KOL and firms, KMC, and ambidextrous innovations.