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Rubber-plastic double-layer bush water-lubricated bearings have demonstrated superior performance, while research on their vibration characteristics remains limited. This paper aims to investigate the lubrication and vibration properties of these bearings by experiments and examine the effect of rubber-to-plastic bush thickness ratio on bearing performance.

A water-lubricated journal bearing test rig is constructed, and three bearings with different bush thickness ratios are fabricated. Bush deformation under various loads is measured, and the friction coefficient and axis trajectory under different operating conditions are tested. The vibration responses of the bearings under directional harmonic excitation are studied. The influences of rotational speed, load and rubber-to-plastic bush thickness ratio on the bearing’s lubrication and vibration properties are analyzed.

The friction coefficient of the bearing initially decreases rapidly and subsequently increases gradually as the rotational speed or load increases. The bearing with a thicker rubber bush shows lower displacement amplitudes in its axis trajectory. Under a 45° directed excitation, significant oscillations are observed in the vertical displacement, while the horizontal displacement remains stable. The damping effect of the bearing with a thicker rubber bush is more pronounced.

This paper present the influence of rubber-to-plastic bush thickness ratio on bearing lubrication and vibration performance. The results are valuable for the design of this type of bearing.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-12-2024-0469/

Accurately predicting the installed capacity of wind energy is essential for energy strategic planning, given the growing need for environmental protection worldwide and the quick development of renewable energy. In order to provide an unprecedented high-precision scheme for wind energy installed capacity prediction and to further become the primary driving force in the process of energy planning and decision-making, this research focuses on overcoming the limitations of conventional prediction models and creatively proposes a multi-parameter collaborative optimization GM(1,1) power model. This will help the energy field advance in a more efficient and scientific direction.

The theoretical framework of the fundamental GM(1,1) power model is thoroughly examined in this study and serves as the basis for further optimizations. To unlock the potential of each parameter optimization, single-parameter optimization investigations of the model are conducted from the viewpoints of the fractional optimization, background value optimization and grey action optimization, respectively. Conversely, an inventive multi-parameter collaborative optimization power model is built. The model is given dynamic flexibility by adding time-varying parameters. The sine function and interpolation technique are used to further optimize the background value. The model’s meaning is enhanced by the inclusion of a power exponent. Furthermore, several parameters are cooperatively tuned with the aid of the sophisticated Firefly algorithm, giving the model stronger predictive powers. A multi-dimensional and multi-regional model comparison analysis is formed by selecting the wind energy installed capacity data of North America, Italy, Japan and South Korea for in-depth empirical analysis in order to confirm the model’s validity.

The findings show that the multi-parameter collaborative optimization model (Model 5) has an exceptional in-sample and out-of-sample prediction effect. The relative prediction error MAPEs are 0.41% and 0.31%. It has a clear advantage over the simple GM(1,1) power model and other single optimization models in applications in North America, South Korea, Japan, and Italy. Its seven variable parameters are the reason for this. These factors help create a very accurate prediction effect through joint optimization from multiple perspectives. It is noteworthy that Model 4’s nonlinear optimization of the grey action is impressive. It performs better than background value optimization and fractional-order optimization. Furthermore, according to the model’s prognosis, North America’s installed wind energy capacity is expected to develop linearly and reach 513.214 bn kilowatts in 2035. This gives the planning for energy development in this area a vital foundation.

The novel idea of the multi-parameter collaborative optimization GM(1,1) power model and its clever integration with the firefly method to accomplish parameter optimization constitute the fundamental value of this study. The substantial benefits of multi-parameter optimization in the stability of the prediction effect have been firmly validated by a thorough comparison with the basic and single-optimization models. Like a lighthouse, this novel model illuminates a more accurate path for wind energy installed capacity prediction and offers high-value reference bases for a variety of aspects, including government energy planning, enterprise strategic layout, investor decision-making direction, fostering technological innovation, advancing academic research and developing energy transformation strategies. As a result, it becomes a significant impetus for the growth of the energy sector.

• (1)

  This study proposes a new gray prediction model. Compared with the traditional grey prediction model, the modeling mechanism of this model is optimized.

• (2)

  This study is based on multi-parameter collaborative optimization to achieve the improvement of model prediction effect. The traditional grey model is two-parameter, while the model proposed in this study is seven-parameter collaborative optimization;

• (3)

  In this study, swarm intelligence algorithm-firefly algorithm is used to optimize the hyperparameters, so as to obtain the best cooperative optimization multi-parameter values;

• (4)

  The application of the model is divided into two parts: empirical and application. In the empirical stage, 5 kinds of prediction models are used to predict, which proves that the model proposed in this paper is effective and improves the prediction accuracy. The application part uses the model to forecast the installed wind power capacity in North America, and the future development trend is linear growth, which is expected to double the installed capacity by 2035.

This study proposes a new gray prediction model. Compared with the traditional grey prediction model, the modeling mechanism of this model is optimized.

This study is based on multi-parameter collaborative optimization to achieve the improvement of model prediction effect. The traditional grey model is two-parameter, while the model proposed in this study is seven-parameter collaborative optimization;

In this study, swarm intelligence algorithm-firefly algorithm is used to optimize the hyperparameters, so as to obtain the best cooperative optimization multi-parameter values;

The application of the model is divided into two parts: empirical and application. In the empirical stage, 5 kinds of prediction models are used to predict, which proves that the model proposed in this paper is effective and improves the prediction accuracy. The application part uses the model to forecast the installed wind power capacity in North America, and the future development trend is linear growth, which is expected to double the installed capacity by 2035.

This study uses third-person effect theory to examine the mechanisms of public opinion about self-regulatory efforts to deal with COVID-19 vaccine-related misinformation on social media, focusing on the roles of social undesirability perceptions and misinformation beliefs.

A national survey of 600 US adults from the Qualtrics panel was conducted. The study examines how perceived social desirability and misinformation beliefs moderate the relationship between exposure to misinformation and behavioral responses.

The results show that the perceived disparity in misinformation exposure relates to third-person perception (TPP), which increases support for content moderation and intentions for corrective actions. Perceiving misinformation as socially undesirable strengthens the link between the exposure gap and TPP. Individual beliefs about misinformation are identified as a crucial moderator, reducing the TPP effect on those who have high misinformation beliefs, leading to less support for content moderation and corrective actions.

This research enhances understanding of TPP in the context of COVID-19 vaccine misinformation by highlighting how social undesirability perceptions and misinformation beliefs moderate this effect. It emphasizes the significance of personal misinformation beliefs in shaping attitudes toward content moderation and corrective actions.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-04-2024-0220

Current multi-source image fusion methods frequently overlook the issue of detailed features when employing deep learning technology, resulting in inadequate target feature information. In real-world mission scenarios, such as military information acquisition or medical image enhancement, the prominence of target feature information is of paramount importance. To address these challenges, this paper introduces a novel infrared-visible light fusion model.

Leveraging the foundational architecture of the traditional DenseFuse model, this paper optimizes the backbone network structure and incorporates a Unique Feature Encoder (UFE) to meticulously extract the distinctive features inherent in the two images. Furthermore, it integrates the Convolutional Block Attention Module (CBAM) and the Squeeze and Excitation Network (SE) to enhance and replace the original spatial and channel attention mechanisms.

Compared to other methods such as IFCNN, NestFuse, DenseFuse, etc., the values of entropy, standard deviation, and mutual information index of the method presented in this paper can reach 6.9985, 82.6652, and 13.6022, respectively, which are significantly improved compared with other methods.

This paper presents a UFEFusion framework that synergizes with the CBAM attention mechanism to markedly augment the extraction of detailed features relative to other methods. Moreover, the framework adeptly extracts and amplifies unique features from disparate images, thereby elevating the overall feature representation capability.

Machine learning (ML) technologies are increasingly being applied in building cost estimation as an advanced method to overcome the challenge of insufficient data and subjective effects of experts. To address the gap of lacking a review of ML applications in building cost estimation, this research aimed to conduct a systematic literature review to provide a robust reference and suggest development pathways for creating novel ML-based building cost prediction models, ultimately enhancing construction project management capabilities.

A systematic literature review according to preferred reporting items for systematic reviews and meta-analyses (PRISMA) was adopted using quantitative bibliographic analysis and qualitative narrative synthesis based on the 70 screened publications from Web of Science (WOS) and Scopus databases. The VOSviewer software was used to prepare the thematic focus from the bibliographic data garnered.

Based on the results of a bibliographic analysis, current research hotspots and future trends in the application of ML to building cost estimation have been identified. Additionally, the mechanisms behind existing ML models and other key points were analyzed using narrative synthesis. Importantly, the weaknesses of current applications were highlighted and recommendations for future development were made. These recommendations included defining the availability of building attributes, increasing the application of emerging ML algorithms and models to various aspects of building cost estimation and addressing the lack of public databases.

The findings are instrumental in aiding project management professionals in grasping current trends in ML for cost estimation and in promoting its adoption in real-world industries. The insights and recommendations can be utilized by researchers to refine ML-based cost estimation models, thereby enhancing construction project management. Additionally, policymakers can leverage the findings to advocate for industry standards, which will elevate technical proficiency and ensure consistency.

Compared to previous research, the findings revealed research hotspots and future trends in the application of ML cost estimation models in only building projects. Additionally, the analysis of the establishment mechanisms of existing ML models and other key points, along with the developed recommendations, were more beneficial for developing improved ML-based cost estimation models, thereby enhancing project management capabilities.

The increasing popularity of live-streaming commerce has provided a new opportunity for e-retailers to boost sales. This study integrated signaling theory and social exchange theory to investigate how streamer- and product-centered signals influence customers’ likelihood of making an impulsive purchase in the live-streaming commerce context.

An online survey was designed and distributed to the target respondents in China using purposive sampling. A total of 735 valid responses were analyzed with partial least square structural equation modeling (PLS-SEM).

Both streamer-centered signals, i.e. streamer credibility and streamer interaction quality, were discovered to significantly influence product-centered signal, i.e. product information quality. Additionally, streamer interaction quality was found to have a significant impact on streamer credibility. Furthermore, it was observed that customer engagement played a significant mediating role in the relationship between product information quality and impulsive buying tendency. Moreover, the paths between product information quality and customer engagement, as well as the connection between engagement and impulsive buying tendency, were found to be moderated by guanxi orientation.

Despite the prevalence of impulsive purchases in live-streaming commerce, few studies have empirically investigated the impact of streamer and product signals on influencing customers’ impulsive purchase decisions. Consequently, to the best of our knowledge, this study distinguishes itself by offering empirical insights into how streamers use reciprocating relationship mechanisms to communicate signals that facilitate impulsive purchase decisions.

The study aims to explore how fashion visual symbols influence consumers' inclination for repurchasing. It attempts to investigate the intricate interplay among three essential variables (social presence, collective excitement and cultural identification) from the perspective of Interaction Ritual Chains theory. Meanwhile, an attempt is made to reveal the underlying patterns in these relationships, fully harnessing the positive impact of fashion brand visual symbols in brand marketing.

This study employs a quantitative research methodology, administering an online survey in China, from which 381 valid responses were collected by simple random sampling. The acquired data were subjected to structural equation model and hypotheses testing.

The analysis reveals that heightened visual symbol perception significantly strengthens consumers' social presence, consequently elevating the probability of collective excitement. This establishes a mediated chain model, reinforcing repurchase intention. Additionally, the moderation effect analysis indicates that cultural identification negatively moderates both direct paths in the mediated chain model, with particularly pronounced effects for low cultural identification.

This study establishes a closed-loop system in fashion brand product marketing, continuously enhancing the intimacy and interactive willingness between consumers, as well as between consumers and the brand. The objective is to increase brand repurchase rates. Additionally, the research provides valuable recommendations and strategies for fashion brands to adapt to Chinese consumer demands, strengthen emotional attachment between consumers and the brand, and achieve sustainable development in the realm of fashion consumption.

This research deals with the critical factors of quality and their significance to perceived security as an effort to build a formidable loyalty of customers in a technology driven service environment. The primary purpose of this study is to investigate the key contributing elements of functional and service quality on perceived security and to further analyze its impact on customer satisfaction and loyalty.

A hypothesized structure model is proposed and justified using the data collected in the manufacturing and telecommunication service sectors in South Korea through survey with total 647 respondents. To clarify the reliability of model and validity of the proposed hypothesis, AMOS 24.0 software was utilized. Furthermore, two moderating variables were adopted for the depth understanding.

This study reveals that service quality dominantly influences the level of perceived security due to its characteristics that are mostly formed on the stage of customer-contact activity. This study further provides a strategic methodology for manufacturing and telecommunication firms to foster sustainable growth by focusing on perceived security during the service delivery process.

This finding is particularly important as Ontact technology becomes increasingly critical during the COVID-19 pandemic. It is worthwhile noting that the research outcome of this study may, in turn, trigger the trust issue that need to be combined with quality in the era of Industry 4.0.

This paper takes the Shanghai-Shenzhen-Hong Kong Stock Connect as a quasi-natural experiment and investigates the impact of capital market liberalization on the corporate debt maturity structure. It also aims to provide some policy implications for corporate debt financing and further liberalization of the capital market in China.

Employing the exogenous event of Shanghai-Shenzhen-Hong Kong Stock Connect and using the data of Chinese A-share firms from 2010 to 2020, this study constructs a difference-in-differences model to examine the relationship between capital market liberalization and corporate debt maturity structure. To validate the results, this study performed several robustness tests, including the parallel test, the placebo test, the Heckman two-stage regression and the propensity score matching.

This paper finds that capital market liberalization has significantly increased the proportion of long-term debt of target firms. Further analyses suggest that the impact of capital market liberalization on the debt maturity structure is more pronounced for firms with lower management ownership and non-Big 4 audit. Channel tests show that capital market liberalization improves firms’ information environment and curbs self-interested management behavior.

This research provides empirical evidence for the consequences of capital market liberalization and enriches the literature on the determinants of corporate debt maturity structure. Further this study makes a reference for regulators and financial institutions to improve corporate financing through the governance role of capital market liberalization.

This study aims to investigate the effects and implications of overconfidence in a competitive game involving multiple newsvendors. This study explores how overconfidence influences system coordination, optimal stocking strategies and competition among newsvendors in the context of the well-known newsvendor stocking problem.

The study applies robust optimization theory and the absolute regret minimization criterion to analyze the competitive game of overconfident newsvendors. This study considers the asymmetric information held by newsvendors regarding market demand and obtains a closed-form solution for the competing game. The effects of overconfidence on system coordination and optimal stocking strategies are examined.

The results of the study indicate that overconfidence can act as a positive force in reducing the effects of overstocking caused by competition and asymmetric information among newsvendors. The analysis reveals that there exists an optimal level of overconfidence that coordinates the ordering system of multiple overconfident newsvendors, leading to first-best outcomes under certain conditions. Additionally, numerical examples confirm the obtained results. Furthermore, considering newsvendors' expected profit, the study finds that a higher degree of overconfidence does not necessarily result in lower actual expected profit.

Despite the significant contributions of this study to theoretical and managerial insights, this study does have certain limitations. First, in the establishment of the belief demand function, the substitution ratio, which quantifies the transfer, is assumed to be an exogenous variable. However, in reality, this is often influenced by factors such as the price of goods and the distance between stores. Therefore, one direction worth studying in the future is to explore the uncertainty associated with the demand substitution ratio and integrate that as an endogenous variable into the optimization model. Second, this study does not address the type of product and solely focuses on quantitatively analyzing the effect of salvage value on the optimal stocking strategy. Future studies can explore the effect of degree of perishability and selling period of the product on the stocking. Third, the focus of uncertainty in this study revolves around market demand, and the implications of this uncertainty are significant. A recent study (Rahbari et al., 2023) addressed an innovative robust optimization problem related to canned foods during pandemic crises. The recent study's findings highlighted the effectiveness of expanding canned food exports to neighboring countries with economic justification as the best strategy for companies amidst the disruptions caused by the coronavirus disease 2019 (COVID-19) pandemic. Incorporating the issue of disruptions into the authors' research would be interesting and challenging.

From a managerial perspective, the authors' study provides a research paradigm for game-theoretic inventory problems in scenarios where the market demand distribution is unknown. While most inventory problems are analyzed and solved based on expectation-based optimization criteria, which rely on an accurate distribution of market demand, obtaining this information in practice can often be challenging or expensive for decision-makers. Consequently, a discrepancy arises between real-world observations and theoretical identifications. This study aimed to complement previous research and address the inconsistency between observations and theoretical identification.

The authors' research contributes to the existing understanding of overconfidence and assists individuals in making appropriate stocking strategies based on the individuals' level of overconfidence. Diverging significantly from the traditional view of overconfidence as a negative bias, the authors' results show the view's potential positive impact within a competitive environment, resulting in greater actual expected profits for newsvendors.

This study contributes to the existing literature by examining the effects of overconfidence in a competitive game of newsvendors. This study extends the analysis of the well-known newsvendor stocking problem by incorporating overconfidence and considering the implications for system coordination and competition. The application of robust optimization theory and the absolute regret minimization criterion provides a novel approach to studying overconfidence in this context.