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Seaports seek to find innovative technological solutions to deal with the ever-increasing growth of transportation and increasing the intensity of competition through the use of emerging technology such as digital twin technology to improve the quality of their logistics operations. Despite the success of digital twins in many industries, there is still a lack of their application in the field of seaports where ports play a central role as part of global transportation chains. Seaports sustainability comprises three main aspects: the social aspect that encompasses more job opportunities, the economic aspect that enhances foreign trade, and the environmental aspect that refers to the process of managing and operating ports in a way that saves the environment. This chapter discusses how to apply digital twins’ technology on the imported Twenty equivalent foot unit (TEUs) taking into consideration the population growth, and the capacity of the storage area of the container terminals in an attempt to explore the impact on Kuwait’s seaports sustainability. The study provides a framework for capacity management in an attempt to initiate the next generation of smart seaports cities and consequently impact society, economy, and well-being in Kuwait and Gulf region. The results of the study showed that there is a strong correlation between population growth and imported TEUs growth during the essential stage of the study. The correlation factor was 0.97, and this correlation will support the prediction until Kuwait vision 2040.

In this chapter, the authors critically examine the application of unmeasured latent method factors (ULMFs) in human resource and organizational behavior (HROB) research, focusing on addressing common method variance (CMV). The authors explore the development and usage of ULMF to mitigate CMV and highlight key debates concerning measurement error in the HROB literature. The authors also discuss the implications of biased effect sizes and how such bias can lead HR professionals to oversell interventions. The authors provide evidence supporting the effectiveness of ULMF when a specific assumption is held: a single latent method factor contributes to the data. However, the authors dispute this assumption, noting that CMV is likely multidimensional; that is, it is complex and difficult to fix with statistical methods alone. Importantly, the authors highlight the significance of maintaining a multidimensional view of CMV, challenging the simplification of a CMV as a single source. The authors close by offering recommendations for using ULMFs in practice as well as more research into more complex forms of CMV.

The paper aims to accurately measure the key motion parameters, such as velocity, azimuth and pitch angle, of the small flying object with a non-uniform curve trajectory. It proposes a measurement method and its calculation model of non-uniform curve trajectory using a photoelectric sensor array.

First, the basic composition of the measurement system and mechanism of photoelectric sensor array are described, respectively. Second, a non-uniform curve mathematical measurement model is constructed differently from the traditional linear trajectory, taking into account the influence of gravity and air resistance. Third, the measurement error of the system is analyzed through numerical simulation. Finally, the accuracy and feasibility of the approach are verified by live-ammunition experiments.

The results show that the systematic error of the hitting point coordinates can be reduced by 9% compared to the traditional linear measurement model. Consequently, this method can meet the higher measurement requirement for the key motion parameters of the small flying object under the non-uniform curve trajectory. Research limitations/implications (if applicable)- although the approach itself is generalizable, the method is unable to detect the motion parameters of multiple small flying objects.

Although the approach itself is generalizable, the method is unable to detect the motion parameters of the multiple small flying objects.

It is evident that the proposed non-uniform curve measurement model is more precise in quantifying the essential characteristics of the small flying object, particularly in consideration of the environmental conditions.

The precise measurement of the key motion parameters of the small flying object can facilitate the enhancement of the protective performance of protective materials.

A novel approach to measurement is proposed, which differs from the conventional uniform trajectory model. To this end, the space construction of the photoelectric sensor array is optimized. The number of the sensors is revised.

Parameter estimation in complex engineering structures typically necessitates repeated calculations using simulation models, leading to significant computational costs. This paper aims to introduce an adaptive multi-output Gaussian process (MOGP) surrogate model for parameter estimation in time-consuming models.

The MOGP surrogate model is established to replace the computationally expensive finite element method (FEM) analysis during the estimation process. We propose a novel adaptive sampling method for MOGP inspired by the traditional expected improvement (EI) method, aiming to reduce the number of required sample points for building the surrogate model. Two mathematical examples and an application in the back analysis of a concrete arch dam are tested to demonstrate the effectiveness of the proposed method.

The numerical results show that the proposed method requires a relatively small number of sample points to achieve accurate estimates. The proposed adaptive sampling method combined with the MOGP surrogate model shows an obvious advantage in parameter estimation problems involving expensive-to-evaluate models, particularly those with high-dimensional output.

A novel adaptive sampling method for establishing the MOGP surrogate model is proposed to accelerate the procedure of solving large-scale parameter estimation problems. This modified adaptive sampling method, based on the traditional EI method, is better suited for multi-output problems, making it highly valuable for numerous practical engineering applications.

As the risk of uncertain quality of used products potentially hinders remanufacturing, this study aims to examine the impact of risk aversion under quality uncertainty of used products in a remanufacturing supply chain (RSC) consisting of a manufacturer and an independent remanufacturer.

We develop an RSC model where the manufacturer produces new products, outsources remanufacturing to the independent remanufacturer and sells both new and remanufactured products to end consumers. Using a manufacturer-led Stackelberg game framework, we derive the equilibrium solutions under risk-neutral and risk-averse scenarios. Additionally, we design a two-part tariff contract to achieve coordination.

We show that while risk aversion leads the manufacturer to raise the outsourcing fee, which in turn reduces both the remanufactured quantity and the collection rate of used products. Consequently, consumer surplus and social welfare decline, while environmental impacts rise. The proposed two-part tariff contract can improve the collection rate and social welfare. We also explore two extensions: an authorization remanufacturing scenario and a two-period scenario. We find that risk aversion has no impact on the selection of remanufacturing mode and the equilibria in the first period. Our findings provide timely managerial insights for RSC management.

One of the main risks deterring remanufacturing is the quality uncertainty of used products. However, the risk aversion arising from this uncertainty and its effects have rarely been studied within a game-theoretic framework. This paper fills this gap by analyzing the remanufacturer’s risk aversion under quality uncertainty and investigating its impacts.

It is desired to provide a diversified iterative scheme for solving the constrained solutions of the generalized coupled discrete-time periodic (GCDTP) matrix equations from the perspective of optimization.

The paper considers generalized reflexive solutions of the GCDTP matrix equations by applying the Jacobi gradient-based iterative (JGI) algorithm, which is an extended variant of the gradient-based iterative (GI) algorithm.

Through numerical simulation, it is verified that the efficiency and accuracy of the JGI algorithm are better than some existing algorithms, such as the GI algorithm in Hajarian, the RGI algorithm in Sheng and the AGI algorithm in Xie and Ma.

It is the first instance in which the GCDTP matrix equations are solved applying the JGI algorithm.

Polyaniline (PANI) has garnered attention for its potential applications in anticorrosion fields because of its unique properties. Satisfactory outcomes have been achieved when using PANI as a functional filler in organic coatings. More recently, research has extensively explored PANI-based organic coatings with self-healing properties. The purpose of this paper is to provide a summary of the active agents, methods and mechanisms involved in the self-healing of organic coatings.

This study uses specific doped acids and metal corrosion inhibitors as active and self-healing agents to modify PANI using the methods of oxidation polymerization, template synthesis, nanosheet carrier and nanocontainer loading methods. The anticorrosion performance of the coatings is evaluated using EIS, LEIS and salt spray tests.

Specific doped acids and metal corrosion inhibitors are used as active agents to modify PANI and confer self-healing properties to the coatings. The coatings’ active protection mechanism encompasses PANI’s own passivation ability, the adsorption of active agents and the creation of insoluble compounds or complexes.

This paper summarizes the active agents used to modify PANI, the procedures used for modification and the self-healing mechanism of the composite coatings. It also proposes future directions for developing PANI organic coatings with self-healing capabilities. The summaries and proposals presented may facilitate large-scale production of the PANI organic coatings, which exhibit outstanding anticorrosion competence and self-healing properties.

This study aims to establish a reliable index to identify interdisciplinary breakthrough innovation effectively. We constructed a new index, the D_Div index, for this purpose.

The D_Div index incorporates the degree of interdisciplinarity in the breakthrough index. To validate the index, a data set combining the publication records and citations of Nobel Prize laureates was divided into experimental and control groups. The validation methods included sensitivity analysis, correlation analysis and effectiveness analysis.

The sensitivity analysis demonstrated the D_Div index’s ability to differentiate interdisciplinary breakthrough papers from various categories of papers. This index not only retains the strengths of the existing index in identifying breakthrough innovation but also captures interdisciplinary characteristics. The correlation analysis revealed a significant correlation (correlation coefficient = 0.555) between the interdisciplinary attributes of scientific research and the occurrence of breakthrough innovation. The effectiveness analysis showed that the D_Div index reached the highest prediction accuracy of 0.8. Furthermore, the D_Div index outperforms the traditional DI index in terms of accuracy when it comes to identifying interdisciplinary breakthrough innovation.

This study proposed a practical and effective index that combines interdisciplinary and disruptive dimensions for detecting interdisciplinary breakthrough innovation. The identification and measurement of interdisciplinary breakthrough innovation play a crucial role in facilitating the integration of multidisciplinary knowledge, thereby accelerating the scientific breakthrough process.

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 study explores the role of industrial co-agglomeration and digital economy in influencing the green high-quality development of the Yellow River National Cultural Park to provide countermeasures and suggestions for promoting the whole-area high-quality development.

This study is based on panel data from 56 cities from 2010 to 2022. First, a Super-SBM model is built to evaluate green high-quality development. Secondly, location entropy is used to measure industrial co-agglomeration and the entropy weight method is used to measure the digital economy. Finally, the panel Tobit model is used to analyze the impact of industrial co-agglomeration and digital economy on the green high-quality development of Yellow River National Cultural Park.

This study found that (1) industrial co-agglomeration has a negative implication in green high-quality development, while the digital economy boosts green high-quality development; (2) industrial co-agglomeration is a less critical dependency on the level of development of the digital economy in influencing green high-quality development, while the facilitating effect of the digital economy is more dependent on industrial co-agglomeration and (3) the trend of slow growth in industrial co-agglomeration and digital economy development, with significant regional differences in green high-quality development.

Undeniably, our study has several limitations. Firstly, as the study area only includes some cities in individual provinces, such as Qinghai, this paper only analyzes at the city level, which does not better reflect the differences between provinces; secondly, this study only adopts one method to determine the digital economy. In the future, other methods can be explored to measure digital economy; finally, in addition to the main role of digital economy and industrial co-agglomeration, other factors may also affect the green high-quality development of YRNCP. Future research should introduce other variables to improve the theoretical framework.

First, it provides countermeasures and suggestions for promoting the green high-quality development of YRNCP. Second, it helps to implement the new development concept, cultivate the new quality productivity of culture and the tourism industry and promote the green high-quality development of YRNCP. Third, it provides references to improve the management measures and related policies of the YRNCP more accurately and efficiently. Fourth, it helps to build a new development pattern and has important practical significance in promoting the high-quality development of the whole basin, protecting and inheriting the Yellow River Culture and helping the Chinese-style modernization and development, which are of great practical significance.

The research is carried out from the new perspective of industrial co-agglomeration and digital economy, which provides the theoretical basis and reference for solving the problem of green high-quality development of YRNCP. Second, it broadens the research idea of green high-quality development. Third, it quantitatively analyzes the impact of industrial co-agglomeration and digital economy on the high-quality development of YRNCP, deepening the research on the green high-quality development of YRNCP. Fourth, it helps to enrich and improve the theoretical research related to the national cultural park development and has positive significance in promoting the management and innovation of the cultural industry and the construction of related disciplines.

The paper’s findings illustrate the functional relationship of the digital economy and industrial co-agglomeration with green high-quality development and propose countermeasures to facilitate the high-quality development of the Yellow River National Cultural Park.