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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/

This study aims to find the impact of supply chain certification (SCCert) on supply chain efficiency (SCEffi) with the inverted U-shaped moderator role of supply chain complexity (SCComp).

In order to test the conceptual model and the hypothesized relationships between all the constructs, the 307 useable survey responses were collected using the purposive sampling technique on a seven-point Likert scale. The SPSS26.0 and AMOS24.0 were used to analyze data, and the hierarchical regression analysis was used to test the model.

This study reached a set of interesting results where it was confirmed that there is a significant relationship between SCCert and SCEffi. It further confirmed the inverted U-shaped moderating effect of SCComp between SCCert and SCEffi: on the left side of the threshold, the increase of SCComp will enhance the promotion effect of SCCert on SCEffi, while on the right side of the threshold, excessive SCComp will rather weaken the promotion effect of SCCert on SCEffi.

The findings provide implications for supply chain efficiency enablers to introduce/promote certification upgrading actions. The study provides a framework for solving the power and constraint problem of supply chain efficiency change.

Findings provide deeper and new insights into threshold feature of supply chain complexity, analyzing how supply chain certification activity realize supply chain efficiency reform through the moderating role of supply chain complexity.

The purpose of this study is to explore the interfacial adhesion between superhydrophobic coatings FC-X (X = 1%, 2%, 3%, 4% and 5%) and the concrete substrate, along with the impact of FC-X on the water repellency characteristics of the concrete substrate.

One synthetic step was adopted to prepare novel F-SiO₂ NP hybrid fluororesin coating. The impact of varying mass fractions of F-SiO₂ NPs on the superhydrophobicity of FC-X was analyzed and subsequently confirmed through water contact angle (WCA) measurements. Superhydrophobic coatings were simply applied to the concrete substrate using a one-step spraying method. The interfacial adhesion between FC-X and the concrete substrate was analyzed using tape pasting tests and abrasion resistance measurements. The influence of FC-X on the water repellency of the concrete substrate was investigated through measurements of water absorption, impermeability and electric flux.

FC-4% exhibits excellent superhydrophobicity, with a WCA of 157.5° and a sliding angle of 2.3°. Compared to control sample, FC-X exhibits better properties, including chemical durability, wear resistance, adhesion strength, abrasion resistance, water resistance and impermeability.

This study offers a thorough investigation into the practical implications of enhancing the durability and water repellency of concrete substrates by using superhydrophobic coatings, particularly FC-4%, which demonstrates exceptional superhydrophobicity alongside remarkable chemical durability, wear resistance, adhesion strength, abrasion resistance, water resistance and impermeability.

Through the examination of the interfacial adhesion between FC-X and the concrete substrate, along with an assessment of FC-X’s impact on the water repellency of the concrete, this paper provides valuable insights into the practical application of superhydrophobic coatings in enhancing the durability and performance of concrete materials.

This study aims to investigate the information-seeking behaviours of home buyers – primarily owner-occupants – using digital real estate platforms, a key element in the industry’s shift towards digital services. It focuses on first-time buyers and repurchasers to examine how these platforms assist in the home-buying process and influence buyer behaviour in Taiwan.

A mixed methods approach was adopted, combining quantitative surveys and qualitative interviews to gather comprehensive data on user experiences and preferences.

The research identifies brand perception, search functionality and search results as critical factors influencing platform usage. Furthermore, it reveals an increasing demand for innovative artificial intelligence-driven search features to enhance user experience and platform convenience, reflecting evolving user expectations.

By addressing the specific context of Taiwan’s real estate market, this study provides novel insights into the interplay between digital platform features and user behaviour. The findings offer practical recommendations for improving platform design to better align with user needs.

With technological progress, retail companies must understand how to make virtual channels and environments more interactive to enhance user engagement. In this study, we apply the stimulus-organism-response (SOR) paradigm and the Customer Engagement Theory to investigate the impact of various types of customer engagement on the connection amid interactivity and continuous metaverse-empowered marketing channel usage.

The analysis was conducted using PLS-SEM on data collected via a representative sampling approach from 300 respondents on prolific online (the UK was fed as the country with the sample representing people with AR/VR gear).

It has been found that cognitive, emotional and social engagement positively and significantly mediates the relation between the variables studied. Personalization had a significantly negative moderating influence on the association between cognitive engagement and continuous use.

Besides extending existing research in the area of metaverse-empowered channels, the study’s findings provide important guidelines for retail companies to enhance customer engagement in virtual environments effectively.

The spectrum resources are becoming increasingly scarce and underutilized, and cooperative spectrum sensing (CSS) in cognitive wireless sensor networks (CWSNs) offers many solutions with good results, but this paper aims to address the significant issue of CSS in the context of low signal-to-noise ratio (SNR).

This study proposes Pearson Correlation Coefficient (PCC) to obtain value feature values under the Rayleigh channel model, which are then used for Memorial K-means Clustering (MKC) analysis of CSS in CWSNs at low SNR regimes. In addition, MKC algorithm is used for training and converted it into supervised model.

A series of numerical simulation results demonstrate that the correctness and effectiveness of the proposed MKC, especially the detection and false alarm probabilities in a low SNR condition. The detection probability is increased by 5%–12% at low SNR compared with other methods.

The MKC algorithm can reduce the impact of randomness on the clustering centers for multiple groups, which combined with PCC can effectively reduce the influence of noise at low SNR, and the unsupervised transformed model effectively reducing the complexity of re-discrimination.

This study aims to investigate the effect of different microstructures and its grain boundary character distribution (GBCD) on the corrosion behavior of weathering bridge steel.

The rust layer characteristics and corrosion resistance of specimens with different microstructures in the simulated industrial environment were studied by Electron Probe X-ray Micro-Analyzer, wavelength-dispersive spectrometer and electrochemical techniques. Electron backscatter diffraction technique was used to characterize the GBCD in steels with different microstructures.

Results revealed a significant difference in the corrosion susceptibility among the four microstructures, with corrosion rates decreasing in the following order: ferrite + pearlite > ferrite + bainite > bainite > martensite. The variation in corrosion resistance is primarily influenced by the microstructure type and the proportion of special grain boundaries, rather than the alloying elements. The proportion of Σ3 boundaries within the coincidence site lattice boundaries is positively correlated with improved corrosion resistance. A higher Σ3 boundary fraction resulted in a lower effective grain boundary energy, elevated self-corrosion potential, increased polarization resistance and reduced areas of localized galvanic corrosion; this led to enhanced inhibition of the electrochemical corrosion reaction, consequently reducing the corrosion rate.

This study elucidates and quantifies the intrinsic relationship between microstructure, GBCD and corrosion rate. This understanding is crucial for enhancing the corrosion resistance of weathering bridge steels in industrial atmospheric corrosion environments.

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.

This study explores the interplay between the spirit of place, brand equity and tourists’ travel intentions within the context of culinary tourism at destinations influenced by Confucian culture. It examines how Confucian values, such as benevolence, righteousness, courtesy, wisdom and trust, shape the personality of culinary tourism destinations and how this personality influences tourists’ travel intentions both directly and indirectly through brand equity.

The researchers develop a conceptual framework highlighting dimensions of destination personality derived from Confucian values. These values serve as indicators of brand equity, focusing on dimensions like brand image, perceived quality and brand loyalty in the culinary tourism sector. Partial least squares structural equation modeling (PLS-SEM) was employed to analyze data from 503 tourists, examining the relationships between the Confucian-inspired destination personality, brand equity and tourists’ travel intentions.

The results show that tourists resonate strongly with the five Confucian personality traits, which significantly influence their travel intentions either directly or indirectly through enhanced brand equity. The study highlights the pivotal role of Confucian culture in shaping the destination personality and brand equity, which, in turn, drives tourists’ travel behaviors.

This study reveals how the spirit of place influences tourist behavior through brand equity, providing new perspectives and practical strategies for destination branding and development.

This study is the first to examine the relationships between spirit of place, brand equity and travel intentions within a Confucian cultural context, expanding the theoretical understanding of culinary tourism branding.

Monitoring of the quality of precast concrete (PC) components is crucial for the success of prefabricated construction projects. Currently, quality monitoring of PC components during the construction phase is predominantly done manually, resulting in low efficiency and hindering the progress of intelligent construction. This paper presents an intelligent inspection method for assessing the appearance quality of PC components, utilizing an enhanced you look only once (YOLO) model and multi-source data. The aim of this research is to achieve automated management of the appearance quality of precast components in the prefabricated construction process through digital means.

The paper begins by establishing an improved YOLO model and an image dataset for evaluating appearance quality. Through object detection in the images, a preliminary and efficient assessment of the precast components' appearance quality is achieved. Moreover, the detection results are mapped onto the point cloud for high-precision quality inspection. In the case of precast components with quality defects, precise quality inspection is conducted by combining the three-dimensional model data obtained from forward design conversion with the captured point cloud data through registration. Additionally, the paper proposes a framework for an automated inspection platform dedicated to assessing appearance quality in prefabricated buildings, encompassing the platform's hardware network.

The improved YOLO model achieved a best mean average precision of 85.02% on the VOC2007 dataset, surpassing the performance of most similar models. After targeted training, the model exhibits excellent recognition capabilities for the four common appearance quality defects. When mapped onto the point cloud, the accuracy of quality inspection based on point cloud data and forward design is within 0.1 mm. The appearance quality inspection platform enables feedback and optimization of quality issues.

The proposed method in this study enables high-precision, visualized and automated detection of the appearance quality of PC components. It effectively meets the demand for quality inspection of precast components on construction sites of prefabricated buildings, providing technological support for the development of intelligent construction. The design of the appearance quality inspection platform's logic and framework facilitates the integration of the method, laying the foundation for efficient quality management in the future.