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The rapid global changes we are witnessing pose a pressing challenge that necessitates reevaluating conventional supply chain practices. Consequently, the integration of digital technologies into supply chain operations, often referred to as digital supply chain (DSC), has emerged as a strategic shift that aims to empower organizations to proactively seize new opportunities rather than being caught off guard by unforeseen disruptions arising from economic volatility, global pandemics and regional conflicts. Thus, this study embraces a knowledge-centric approach to explore the direct and indirect impact of knowledge management, innovation and learning capabilities on DSC adoption in an emerging economy context. Furthermore, it aims to shed light on the moderating role of environmental dynamism in this intricate interplay.

Employing a cross-sectional survey, the research data were collected from 354 managers representing Egyptian manufacturing and service firms utilizing a structured questionnaire. Data analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM).

The results unveiled that knowledge management capability (KMC) has the highest path coefficient value among all endogenous variables. It accounts for a significant portion of the variance in innovation and learning capabilities, which play pivotal roles in adopting DSC. Notably, learning capability appears to exert a more powerful influence on DSC adoption than innovation capability through direct and mediating effects. Furthermore, the findings underscore the moderating effect of environmental dynamism on the relationship between learning capability and DSC adoption. However, this moderating role is not observed in the link between innovation capability and DSC adoption.

There is a growing trend among firms to adopt DSC in response to significant environmental shifts. This study offers valuable insights for managers and policymakers, providing them with a deeper understanding of the DSC adoption process. The study’s findings assist in identifying crucial factors that boost DSC adoption and offer guidance on successfully leveraging digital technologies for managing supply chain practices. Moreover, the study offers stimulating directions for future DSC research directions.

The study contributes to the existing literature by expanding our understanding of the adoption of DSC by utilizing knowledge, innovation and learning capabilities within the context of emerging economies.

Sustainable urban rail transit requires noise barriers. However, these barriers’ durability varies due to the differing aerodynamic impacts they experience. The purpose of this paper is to investigate the aerodynamic discrepancies of trains when they meet within two types of rectangular noise barriers: fully enclosed (FERNB) and semi-enclosed with vertical plates (SERNBVB). The research also considers the sensitivity of the scale ratio in these scenarios.

A 1:16 scaled moving model test analyzed spatiotemporal patterns and discrepancies in aerodynamic pressures during train meetings. Three-dimensional computational fluid dynamics models, with scale ratios of 1:1, 1:8 and 1:16, used the improved delayed detached eddy simulation turbulence model and slip grid technique. Comparing scale ratios on aerodynamic pressure discrepancies between the two types of noise barriers and revealing the flow field mechanism were done. The goal is to establish the relationship between aerodynamic pressure at scale and in full scale.

The aerodynamic pressure on SERNBVB is influenced by the train’s head and tail waves, whereas for FERNB, it is affected by pressure wave and head-tail waves. Notably, SERNBVB's aerodynamic pressure is more sensitive to changes in scale ratio. As the scale ratio decreases, the aerodynamic pressure on the noise barrier gradually increases.

A train-meeting moving model test is conducted within the noise barrier. Comparison of aerodynamic discrepancies during train meets between two types of rectangular noise barriers and the relationship between the scale and the full scale are established considering the modeling scale ratio.

Coordinating low-carbonization and digitalization is a practical implementation pathway to achieve high-quality economic development. Regions are under great emission reduction pressure to achieve low-carbon development. However, why and how regional emission reduction pressure influences enterprise digital transformation is lacking in the literature. This study empirically tests the impact of emission reduction pressure on enterprise digital transformation and its mechanism.

This article takes the data of non-financial listed companies from 2011 to 2020 as a sample. The digital transformation index is measured by entropy value method. The bidirectional fixed effect model was used to test the hypothesis.

The research results show that emission reduction pressure forces enterprise digital transformation. The mechanism lies in that emission reduction pressure improves digital transformation by promoting enterprise innovation, and digital economy moderates the nexus between emission reduction pressure and digital transformation. Furthermore, the effect of emission reduction pressure on digital transformation is more significant for non-state-owned, mature and high-tech enterprises.

This paper discusses the mediating role of enterprise innovation between carbon emission reduction pressure and enterprise digital transformation, as well as the moderating role of digital economy. The research expands the body of knowledge about dual carbon targets, digitization and technological innovation. The author’s findings help update the impact of regional digital economy development on enterprise digital transformation. It also provides theoretical guidance for the realization of digital transformation by enterprise innovation.

This study aims to enhance the prediction accuracy of hydroelectricity consumption in China, with a focus on addressing the challenges posed by complex and nonlinear characteristics of the data. A novel grey multivariate prediction model with structural optimization is proposed to overcome the limitations of existing grey forecasting methods.

This paper innovatively introduces fractional order and nonlinear parameter terms to develop a novel fractional multivariate grey prediction model based on the NSGM(1, N) model. The Particle Swarm Optimization algorithm is then utilized to compute the model’s hyperparameters. Subsequently, the proposed model is applied to forecast China’s hydroelectricity consumption and is compared with other models for analysis.

Theoretical derivation results demonstrate that the new model has good compatibility. Empirical results indicate that the FMGM(1, N, a) model outperforms other models in predicting the hydroelectricity consumption of China. This demonstrates the model’s effectiveness in handling complex and nonlinear data, emphasizing its practical applicability.

This paper introduces a scientific and efficient method for forecasting hydroelectricity consumption in China, particularly when confronted with complexity and nonlinearity. The predicted results can provide a solid support for China’s hydroelectricity resource development scheduling and planning.

The primary contribution of this paper is to propose a novel fractional multivariate grey prediction model that can handle nonlinear and complex series more effectively.

Predicting commodity futures trading volumes represents an important matter to policymakers and a wide spectrum of market participants. The purpose of this study is to concentrate on the energy sector and explore the trading volume prediction issue for the thermal coal futures traded in Zhengzhou Commodity Exchange in China with daily data spanning January 2016–December 2020.

The nonlinear autoregressive neural network is adopted for this purpose and prediction performance is examined based upon a variety of settings over algorithms for model estimations, numbers of hidden neurons and delays and ratios for splitting the trading volume series into training, validation and testing phases.

A relatively simple model setting is arrived at that leads to predictions of good accuracy and stabilities and maintains small prediction errors up to the 99.273th quantile of the observed trading volume.

The results could, on one hand, serve as standalone technical trading volume predictions. They could, on the other hand, be combined with different (fundamental) prediction results for forming perspectives of trading trends and carrying out policy analysis.

This study investigates the effect of entrepreneurial orientation and government support on the innovation openness of South Korean manufacturing firms. By compartmentalizing entrepreneurial orientation and government support, this study seeks to elucidate their effects on the breadth and depth of firms' knowledge searches.

The study utilizes Poisson regression analysis with empirical data from 2,353 South Korean manufacturing firms collected from Korean Innovation Survey data.

Analysis reveals nuanced influences of various factors on firms' open innovation activities. Innovativeness positively affects knowledge search breadth and depth, highlighting the role of innovation-oriented processes, while competitive aggressiveness influences depth, thereby underscoring the pursuit of specialized knowledge acquisition. Proactiveness positively affects breadth but negatively affects depth, suggesting the importance of maintaining broad perspectives. Government support scope positively affects both breadth and depth, reflecting the significance of external support in encouraging inclusive knowledge exploration. The importance of government support positively influences depth, emphasizing its vital role in establishing collaborative relationships to enhance in-depth knowledge acquisition.

This study provides novel insights by exploring the nuanced implementation of open innovation in latecomer economies, especially South Korea, which experienced rapid economic development by leveraging innovation. The identification of specific factors influencing knowledge search breadth and depth contributes to the literature on open innovation by providing strategic guidelines for firms and policymakers. This practical implications of this study suggest that firms should align their strategies with entrepreneurial orientation while leveraging pertinent government support.

Live-streaming e-commerce (LSE) allows anchors to bring offline promotion skills to interact with consumers and persuade them to buy. However, how consumers respond to these communications remains unknown. This study examines the persuasive effect of different scarcity marketing messages on impulsive buying in the LSE context.

This study adopts scenario-based experimental methods and conducts two 2 quantity-based scarcity (supply-framed vs demand-framed)*2 time-based scarcity appeals (high vs low) between-subjects experiments to test the hypotheses.

The results indicate that supply-framed appeals are more effective in provoking consumers’ arousal and impulsive buying, but are moderated by time scarcity. Furthermore, emotional arousal only mediates the effects of quantity-based scarcity appeals on impulsive buying under high-level time scarcity conditions.

This study contributes to the e-commerce literature by comparing the persuasive effect of different scarcity messages in the LSE context. We broaden the scarcity marketing literature by testing the combined effect of quantity-based and time-based scarcity appeals. Finally, this study extends the application of the competitive arousal model.

The purpose of this paper was to prepare a ternary hierarchical rough particle to accelerate the anti-corrosive design for coastal concrete infrastructures.

A kind of micro-nano hydrophobic ternary microparticles was fabricated from SiO₂/halloysite nanotubes (HNTs) and recycled concrete powders (RCPs), which was then mixed with sodium silicate and silane to form an inorganic slurry. The slurry was further sprayed on the concrete surface to construct a superhydrophobic coating (SHC). Transmission electron microscopy and energy-dispersive X-ray spectroscopy mappings demonstrate that the nano-sized SiO₂ has been grafted on the sub-micron HNTs and then further adhered to the surface of micro-sized RCP, forming a kind of superhydrophobic particles (SiO₂/HNTs@RCP) featured of abundant micro-nano hierarchical structures.

The SHC surface presents excellent superhydrophobicity with the water contact angle >156°. Electrochemical tests indicate that the corrosion rate of mild steel rebar in coated concrete reduces three-order magnitudes relative to the uncoated one in 3.5% NaCl solution. Water uptake and chloride ion (Cl^-) diffusion tests show that the SHC exhibits high H₂O and Cl^- ions barrier properties thanks to the pore-sealing and water-repellence properties of SiO₂/HNTs@RCP particles. Furthermore, the SHC possesses considerable mechanical durability and outstanding self-cleaning ability.

SHC inhibits water uptake, Cl^- diffusion and rebar corrosion of concrete, which will promote the sustainable application of concrete waste in anti-corrosive concrete projects.

The purpose of this review paper is to provide a review of the most recent advances in the field of manufacturing composite sandwich panels along with their advantages and limitations. The other purpose of this paper is to familiarize the researchers with the available developments in manufacturing sandwich structures.

The most recent research articles in the field of manufacturing various composite sandwich structures were reviewed. The review process started by categorizing the available sandwich manufacturing techniques into nine main categories according to the method of production and the equipment used. The review is followed by outlining some automatic production concepts toward composite sandwich automated manufacturing. A brief summary of the sandwich manufacturing techniques is given at the end of this article, with recommendations for future work.

It has been found that several composite sandwich manufacturing techniques were proposed in the literature. The diversity of the manufacturing techniques arises from the variety of the materials as well as the configurations of the final product. Additive manufacturing techniques represent the most recent trend in composite sandwich manufacturing.

This work is valuable for all researchers in the field of composite sandwich structures to keep up with the most recent advancements in this field. Furthermore, this review paper can be considered as a guideline for researchers who are intended to perform further research on composite sandwich structures.

Recent emerging information technologies like digital twin (DT) provide new concepts and transform information management processes in the architecture, engineering and construction (AEC) industry. Although numerous articles are pertinent to DT applications, existing research areas and potential future directions related to the state-of-the-art DT in project operation and maintenance (O&M) are yet to be studied. Therefore, this paper aims to review the state-of-the-art research on DT applications in project O&M.

The current review adopted four methodological steps, including literature search, literature selection, science mapping analysis and qualitative discussion to gain a deeper understanding of DT in project O&M. The impact and contribution of keywords and documents were examined from a total of 444 journal articles retrieved from the Scopus database.

Five mainstream research topics were identified, including (1) DT-based artificial intelligence technology for project O&M, (2) DT-enabled smart city and sustainability, (3) DT applications for project asset management, (4) Blockchain-integrated DT for project O&M and (5) DT for advanced project management. Subsequently, research gaps and future research directions were proposed.

This study intends to raise awareness of future research by summarizing the current DT development phases and their impact on DT implementation in project O&M among researchers and practitioners.