Search results

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.

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.

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.

Nano graphitic-carbon nitride (g-C₃N₄) is an emerging lubrication technology with excellent performance and significant potential for future applications. This study aims to investigate the effect of nano g-C₃N₄ as a lubricant additive on the wear performance of bearing steel disk.

Various mass fractions of g-C₃N₄ were introduced into the base oil. Combining tribological testing, rheological testing and surface analysis methods, the anti-wear properties and lubrication mechanisms were analyzed.

Transmission electron microscopy images revealed that the size of the nanoparticles of g-C₃N₄ ranges from 10 to 100 nm. Phase analysis of the g-C₃N₄ sample was conducted using X-ray diffraction. Further, 1.0% mass fraction of g-C₃N₄ in the base oil provides excellent anti-wear and friction-reducing performance. Compared to the base oil alone, it reduces the average friction coefficient by 63.8% and decreases the wear rate by 43.1%, significantly reducing the depth and width of the wear scar. Energy-dispersive X-ray spectroscopy and scanning electron microscope analysis revealed that the oil sample containing nano g-C₃N₄ can form a lubricating film on the sliding surface of bearing steel after wear, which enhances the lubricating properties of the base oil.

The synergistic effect of the base oil and nanoparticles reduces friction and wear and is expected to extend the service life of bearing steel. These findings suggest that incorporating nano g-C₃N₄ as a lubricant additive offers significant potential for improving the performance of mechanical components.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-12-2024-0456/

The purpose of this study is to investigate the influence of geometric parameters of removable panels on the sealing characteristics of ceramic wafer seal structure subjected to high-temperature gas flow.

Based on the laminar flow Reynolds equation, the theoretical and numerical calculation models were constructed to investigate the influence of external convex deformation of removable panel on leakage rate. The theoretical formula for leakage rate after deformation of the removable panel was derived, and the flow field and leakage characteristics of ceramic wafer seal under different operating parameters were studied.

The leakage rate exhibits consistent trends between theoretical calculation, numerical simulation and experimental value, with a maximum discrepancy of 8.9%. This validates the accuracy of both the theoretical model and numerical simulation. As the deformation angle of the removable panel increases, the sealing gap gradually widens, resulting in a compromised sealing effect. Moreover, the leakage rate in the central region of the sealing area is lower compared to that at both ends.

The leakage of the ceramic wafer seal structure under the removable panel with different deformation angles can be monitored based on Reynolds equation. The pseudo-transient numerical calculation method can be used to determine the leakage value of the micro-state ceramic wafer seal structure. These research findings provide a theoretical foundation and numerical investigation approach for studying ceramic wafer seal structures.

This research develops a comprehensive framework to enhance the functionality of medical equipment in hospitals, focusing on disease diagnosis contexts. By leveraging failure mode and effects analysis (FMEA) and Industry 4.0 tools, it aims to optimize healthcare services and patient treatment outcomes, particularly during crises like pandemics.

Adopting a mixed-methods approach, the research integrates Industry 4.0 and automation principles to develop and validate the framework. Utilizing a four-year failure database analyzed with R Programming, it implements preventive maintenance strategies such as the Preventive Maintenance Management System (PMMS) with FMEA. FMEA is implemented to systematically identify, assess and prioritize failure modes, enabling targeted mitigation strategies and improving equipment reliability. The framework is validated through empirical analysis and case studies, assessing operational efficiency, equipment importance and societal impact, with recommendations for future research on advanced maintenance methodologies.

The framework significantly reduces equipment failure rates (FR) and mean time to repair (MTTR), enhancing maintenance efficiency. Downtime decreases, especially for critical medical equipment like life-saving and diagnostic devices, resulting in a remarkable 95% increase in maintenance efficiency. The framework prioritizes and optimizes interventions for vital equipment, ultimately improving patient care and healthcare services.

The research presents a practical framework for enhancing medical equipment maintenance in Indian hospitals, particularly during disease diagnosis. Leveraging automation technologies, it reduces equipment failure risks, ensuring operational continuity even during pandemics. Improvements in diagnostic accuracy directly benefit patient care, with recommendations aimed at further advancing maintenance methodologies and enhancing healthcare delivery.

The research develops and validates the framework, employing FMEA to identify critical failures and integrating automation (Industry 4.0) to prioritize maintenance tasks. Post-implementation outcomes validate significant improvement, addressing existing gaps in medical equipment maintenance practices. This contributes to optimizing healthcare services and patient outcomes, particularly during critical disease diagnosis scenarios.