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Gross domestic product (GDP) is an important indicator to measure a country's economic development. If the future development trend of a country's GDP can be accurately predicted, it will have a positive effect on the formulation and implementation of the country's future economic development policies. In order to explore the future development trend of China's GDP, the purpose of this paper is to establish a new grey forecasting model with time power term to forecast GDP.

Firstly, the shortcomings of the traditional grey prediction model with time power term are found out through analysis, and then the generalized grey prediction model with time power term is established (abbreviated as PTGM (1,1, α) model). Secondly, the PTGM (1,1, α) model is improved by linear interpolation method, and the optimized PTGM (1,1, α) model is established (abbreviated as OPTGM (1,1, α) model), and the parameters of the OPTGM (1,1, α) model are solved by the quantum genetic algorithm. Thirdly, the advantage of the OPTGM (1,1, α) model over the traditional grey models is illustrated by two real cases. Finally the OPTGM (1,1, α) model is used to predict China's GDP from 2020 to 2029.

The OPTGM (1,1, α) model is more suitable for predicting China's GDP than other grey prediction models.

A new grey prediction model with time power term is proposed.

A new method for forecasting wind turbine capacity of China is proposed through grey modelling technique.

First of all, the concepts of discrete grey model are introduced into the NGBM(1,1) model to reduce the discretization error from the differential equation to its discrete forms. Then incorporating the conformable fractional accumulation into the discrete NGBM(1,1) model is carried out to further improve the predictive performance. Finally, in order to effectively seek the emerging coefficients, namely, fractional order and nonlinear coefficient, the whale optimization algorithm (WOA) is employed to determine the emerging coefficients.

The empirical results show that the newly proposed model has a better prediction performance compared to benchmark models; the wind turbine capacity from 2019 to 2021 is expected to reach 275954.42 Megawatts in 2021. According to the forecasts, policy suggestions are provided for policy-makers.

By combing the fractional accumulation and the concepts of discrete grey model, a new method to improve the prediction performance of the NGBM(1,1) model is proposed. The newly proposed model is firstly applied to predict wind turbine capacity of China.

This paper aim to take the ship shaft stern bearing as the research object, and studies the influence of journal axial vibration on bearing dynamic characteristics under different misaligned angles and rotation speeds.

Computational fluid dynamics (CFD) and harmonic excitation method were used to build bearing unstable lubrication model, and the dynamic mesh technology was used in calculation.

The results indicate that journal axial vibration has a significant effect on bearing dynamic characteristics, like maximum oil film pressure, bearing stiffness and damping coefficients, and the effect is positively correlated with journal misaligned angle. The effect of shaft rotation speed and journal axial vibration on bearing dynamics characteristics are independent; they have no coupling. Bearing axial stiffness is mainly affected by the journal axial displacement, bearing axial damping is mainly affected by journal axial velocity and they are positively correlated with the misaligned angle. The influence of rotational speed on bearing axial stiffness and axial damping is not obvious.

This paper establishes the bearing dynamic model by CFD and harmonic excitation method with consideration of cavitation effect and analyzing the influence of journal axial vibration on the dynamic characteristics. The results are benefit to the design of ship propulsion shaft and the selection of stern bearing. Also, they are of great significance to improve the operation stability of the shaft bearing system and the vitality of the ship.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-11-2022-0337/

Wireless network localization technology is very popular in recent years and has attracted worldwide attention. The purpose of this paper is to improve the localization accuracy of ultra-wideband (UWB) with lower localization error taking into consideration the special real environment with the closed long and narrow space.

The principle of multidimensional scaling (MDS), particle swarm optimization (PSO) and Taylor series expansion algorithm (Taylor-D) were introduced. A novel positioning algorithm, MDS-PSO-Taylor was proposed to minimize the localization error. MDS-PSO algorithm provided a more accurate preliminary coordinate by applying the PSO algorithm so that the Taylor-D was used for further enhancing the localization accuracy.

Experimental results manifested that the proposed algorithm, providing small localization error value and higher positioning accuracy, can effectively reduce errors and achieve better performance in terms of the considerable improvement of localization accuracy.

The presented study with the real environment test attempts to demonstrate the proposed algorithm is hopeful to be applied to the underground environment for in the future.

Manufacturers increasingly resort to digital transformation to shape their competitiveness in the digital economy era, while supply chain (SC) collaborative innovation helps them cope with market uncertainties. However, whether and how digital transformation can facilitate SC collaborative innovation remain unclear. To address this gap, we aims to investigate the effects of digital transformation (strategy and capability) on SC collaborative (process and product) innovation and market performance.

We use partial least squares-structural equation modelling (PLS-SEM) with a sample of 210 Chinese manufacturers to investigate the effects of digital transformation (strategy and capability) on SC collaborative (process and product) innovation and market performance.

The results show that digital strategy and capability positively impact SC collaborative process and product innovation, which enhances market performance. In addition, SC collaborative innovation mediates the relationship between digital transformation and market performance.

This study contributes to the literature by identifying how digital transformation drives SC collaborative innovation towards improving market performance and providing practical guidance for enterprises in promoting digital transformation and SC collaborative innovation.

The purpose of this paper is to properly calibrate the laser tracking system (LTS) prior to using it for metrology and improving the measuring accuracy of LTS.

A kinematics model that describes not only the motion but also geometric variations of LTS is developed. Effects of tracking mirror mechanism dimension errors on measured coordinates and target reflector alignment errors on sensor reading are investigated.

Through error analysis of the proposed model, it is claimed that gimbals axis misalignments and tracking mirror center offset are the key contributors to measure errors of LTS. Intensive simulation studies are conducted to check the validity of the theoretical results and various practical issues are also explored in the simulations. The simulation results demonstrate that under realistic conditions the 10‐parameter model is the minimal and complete model.

This model, together with its error model which is also presented in this paper, can be used for design, calibration, and control of LTS.

This paper develops a kinematics model that describes not only the motion but also geometric variations of LTS, and demonstrates that gimbals axis misalignments and tracking mirror center offset is the key contributor to measuring errors of LTS.

Given the critical role of green innovation (GI) in the manufacturing sector, this study builds a moderated mediation model to evaluate the influence of leadership and management support on GI, the mediating function of green knowledge acquisition, and the moderating role of green absorptive ability.

The study employed a quantitative research approach with hierarchical regression analysis to assess the proposed relationships among the constructs on a sample of 371 executives from 117 large-sized manufacturing firms in Pakistan.

The research findings demonstrate that leadership and management support significantly affects both radical and incremental GI, with incremental green innovation being more positively affected than radical green innovation. Green knowledge acquisition partially mediates between leadership and management support, radical and incremental green innovation. Green knowledge acquisition moderates the association between leadership and management support and green knowledge acquisition and the link between leadership and management support and incremental GI. The findings also demonstrate that green knowledge acquisition's mediating effect on leadership and management support, and GI is more pronounced when green absorptive capacity is high.

This research is based on cross-sectional data gathered from manufacturing companies. Future studies should consider this differentiation between the enterprises since there are various sectors within the general manufacturing sector whose environmental effect is more or less polluting. This research focused exclusively on two aspects of GI (radical and incremental GI). It is feasible that additional GI constituents (i.e., product, process, and management GI) can significantly boost businesses' competitive advantage. This study recommends additional study into the potential moderating impacts of technological and market turbulence to better understand the relationship between these concepts since it is evident that internal and external factors influence GI.

The study provides useful insights and an innovative way for manufacturing firms and authorities to prevent environmental deterioration and achieve sustainable green innovation through leadership and management support and green intangible resources.

This research concentrating on green environmental concerns and using RBV theory attempts to fill research gaps and sheds light on how leadership and management support promote both radical and incremental green innovation via the mediating and moderating roles of green knowledge acquisition and green absorptive capacity.

The purpose of this paper is to investigate the determinants of audit committee meeting frequency in Chinese listed companies.

A multiple linear regression model, derived from the logarithmic model proposed by Raghunandan and Rama, is used to examine the determinants and an unbalanced panel data fixed effects model was used for robust tests.

Based on 912 year‐firm observations, the authors found that audit committee meeting frequency was negatively associated with the proportion of shares owned by a majority shareholder and the number of audit committee meetings is less in stated‐owned firms than privately‐owned firms. Both audit committee and firm size were found to be positively associated with the frequency and there was a negative relationship between the proportion of independent directors on a board of directors and the number of audit committee meetings in China. However, no evidence was found of the associations of the frequency with the proportion of directors who are accounting experts on the audit committee, the CEO‐Chairman duality, management ownership, board size, BIG4 and profitability.

This is the first paper to present empirical evidence on the determinants of audit committee meeting frequency in Chinese listed companies. The paper looks into the impact of firm ownership on the meeting frequency in China and finds that the number of audit committee meetings is less in stated‐owned listed firms than privately‐owned listed firms.

Supply chain resilience and knowledge management (KM) processes have received increasing attention from researchers and practitioners. Nevertheless, previous studies often treat the two streams of literature independently. Drawing on the knowledge-based theory, this study aims to reconcile these two different streams of literature and examine how and when KM processes influence supply chain resilience.

This research develops a conceptual model to test a sample of data from 203 Chinese manufacturing firms using a structural equation modeling method. Specifically, the current study empirically examines how KM processes affect different forms of supply chain resilience (supply chain readiness, responsiveness and recovery) and examines the moderating effect of blockchain technology adaptation and organizational inertia on the relationship between KM processes and supply chain resilience.

The findings show that KM processes positively affect three dimensions of supply chain resilience, i.e., supply chain readiness, responsiveness and recovery. Besides, the study reveals that blockchain technology adoption positively moderates the relationships between KM processes and supply chain resilience, whereas organizational inertia negatively moderates these above relationships.

This research linked the two research areas of supply chain resilience and KM processes, further bridging the gap in the research exploration of KM in the supply chain field. Next, this study contributes to supply chain resilience research by investigating how KM systems positively impact supply chain readiness, responsiveness and recovery. In addition, this study found a moderating effect of blockchain technology adaption and organizational inertia on the relationship between KM processes and supply chain resilience. These findings provide a reference for Chinese manufacturing firms to strengthen supply chain resilience, achieve secure supply chain operations and gain a competitive advantage in the supply chain. This studys’findings advance the understanding of supply chain resilience and provide practical implications for supply chain managers.

Building supply chain resilience is increasingly recognized as an effective strategy to deal with supply chain challenges, risks and disruptions. Nevertheless, it remains unclear how to build supply chain resilience and whether supply chain resilience could achieve a competitive advantage.

By analyzing the data collected from 216 firms in China, the current study empirically examines how information technology (IT) capability and supply chain collaboration affect different forms of supply chain resilience (external resilience and internal resilience) and examines the performance implications of these two forms of supply chain resilience.

Results show that IT capability is positively related to external resilience, whereas supply chain collaboration is positively related to internal resilience. The combination of IT capability and supply chain collaboration is positively related to external resilience. In addition, internal resilience is positively related to firm performance.

This study used only cross-sectional data from China for hypothesis testing. Future studies could utilise longitudinal data and research other countries/regions.

The findings systematically assess how IT capability and supply chain collaboration contribute to supply chain resilience and firm performance. The results provide a benchmark of supply chain resilience improvement that can be expected from IT capability and supply chain collaboration.

The study findings advance the understanding of supply chain resilience and provide practical implications for supply chain managers.