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Visitor center plays an important role in the normal operation and sustainable development of scenic spots, especially as a portal image of its management. This paper presents resilience theory for visitor centers to identify some common issues in designing visitor centers in China scenic spots, including the lack of function, loss of architectural characteristics, and difficultly in adapting to changes in the number of visitors with periodic variations. The framework of resilience theory was set from four dimensions, namely, resilience and match in the composition of ontology function, the extended function, integration of buildings into the surrounding environment, and alternative construction technologies and materials. This theory was explained and analyzed with the application of the theory in practice in combination with the design of Mount Hua visitor center. Results showed that resilience theory yields good application effect.

It is a challenging task to analysis oxide wear particles when they are stuck together with other types of wear particles in complex ferrography images. Hence, this paper aims to propose a method of ferrography image segmentation to analysis oxide wear debris in complex ferrography images.

First, ferrography images are segmented with watershed transform. Then, two region merging rules are proposed to improve the initial segmentation results. Finally, the features of each particle are extracted to detect and assess the oxide wear particles.

The results show that the proposed method outperforms other methods of ferrography image segmentation, and the overlapping wear particles in complex ferrography images can be well separated. Moreover, the features of each separated wear particles can be easily extracted to analysis the oxide wear particles.

The proposed method provides a useful approach for the automatic detection and assessment of oxide wear particles in complex ferrography images.

The colours, edges and position information of wear debris are considered in the proposed method to improve the segmentation result. Moreover, the proposed method can not only detect oxide wear particles in ferrography images but also evaluate oxide wear severity in ferrography images.

This study aims to investigate the synergistic friction reduction and antiwear effects of lyophilized graphene loading nano-copper (RGO/Cu) as lubricating oil additives, compared with graphene.

The friction performance of freeze-drying graphene (RGO) and RGO/Cu particles was investigated at different addition concentrations and under different conditions.

Graphene plays a synergistic friction reduction and antiwear effect because of its large specific surface area, surface folds and loading capacity on the nanoparticles. The results showed that the average friction coefficients of RGO and RGO/Cu particles were 22.9% and 6.1% lower than that of base oil and RGO oil, respectively. In addition, the widths of wear scars were 62.3% and 55.3% lower than those of RGO/Cu particles, respectively.

The RGO single agent is suitable for medium-load and high-speed conditions, while the RGO/Cu particles can perform better in the conditions of heavy load and high speed.

Facility structures in liquefied natural gas (LNG) plants require tremendous amounts of scaffolding to facilitate relevant industrial operation and maintenance. As such, the productivity of scaffolding operations in turnaround maintenance (TAM) has attracted much attention in recent years. In addition, health and safety issues have been recognised as a key contributor along with productivity improvement in the LNG industry. This study aims to integrate work posture analysis into value stream mapping to achieve an optimised and balanced improvement in both productivity and health and safety.

A case study approach is adopted to integrate lean and work posture analysis in a TAM site. The lean improvement is conducted through value stream mapping, and the work posture analysis is conducted through the Ovako Working Posture Analysis System method. A three-step optimisation strategy is then developed for achieving optimised performance in waste reduction and work posture improvement.

It is found that the implementation of value stream mapping can help eliminate waste in the installation process, therefore eliminating potential health and safety risks. However, health and safety of onsite workers does not always improve as lean implementation intensifies. There is an optimised erection schedule that has the lowest health and safety risk within a waste reduction target.

In contradiction to previous studies, which rely on qualitative assessment to identify the a positive correlation between lean and health and safety, this study reveals the distinct difference between lean attributes and health and safety attributes through a quantitative assessment and is more readily to be implemented at the site level for simultaneous improvement in lean and health and safety.

Using computer technology to realize ferrographic intelligent fault diagnosis technology is fundamental research to inspect the operation of mechanical equipment. This study aims to effectively improve the technology of deep learning technology in the field of ferrographic image recognition.

This paper proposes a binocular image classification model to solve ferrographic image classification problems.

This paper creatively proposes a binocular model (BesNet model). The model presents a more extreme situation. On the one hand, the model is almost unable to identify cutting wear particles. On the other hand, the model can achieve 100% accuracy in identifying Chunky and Nonferrous wear particles. The BesNet model is a bionic model of the human eye, and the used training image is a specially processed parallax image. After combining the MCECNN model, it is changed to BMECNN model, and its classification accuracy has reached the highest level in the industry.

The work presented in this thesis is original, except as acknowledged in the text. The material has not been submitted, either in whole or in part, for a degree at this or any other university. The BesNet model developed in this article is a brand new system for ferrographic image recognition. The BesNet model adopts a method of imitating the eyes to view ferrography images, and its image processing method is also unique. After combining the MCECNN model, it is changed to BMECNN model, and its classification accuracy has reached the highest level in the industry.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2023-0150/

The purpose of this research is to provide a theoretical framework for complex equipment quality risk evaluation. The primary aim of the framework is to enhance the ability to identify risks and improve risk control efficiency during the development phase.

A novel framework for quality risk evaluation in complex equipment is proposed, which integrates probabilistic hesitant fuzzy set-quality function deployment (PHFS-QFD) and grey clustering. PHFS-QFD is applied to identify the quality risk factors, and grey clustering is used to evaluate quality risks in cases of poor quality information during the development stage. The unfolding function of QFD is applied to simplify complex evaluation problems.

The methodology presents an innovative approach to quality risk evaluation for complex equipment development. The case analysis demonstrates that this method can efficiently evaluate the quality risks for aircraft development and systematically trace back the risk factors through hierarchical relationships. In comparison to traditional failure mode and effects analysis methods for quality risk assessment, this approach exhibits superior effectiveness and reliability in managing quality risks for complex equipment development.

This study contributes to the field by introducing a novel theoretical framework that combines PHFS-QFD and grey clustering. The integration of these approaches significantly improves the quality risk evaluation process for complex equipment development, overcoming challenges related to data scarcity and simplifying the assessment of intricate systems.

Online reviews increasingly present the characteristic of bidirectional communication with the advent of Web 2.0 era and tend to be asymmetrical and individualized in linguistic information. The authors aim to develop a new linguistic conversion model that exploits the asymmetric and personalized information from online reviews to express such linguistic information. A new online recommendation approach is provided.

The necessity of new linguistic conversation model is elucidated, and a leverage factor is incorporated into the linguistic label of negative review to handle the asymmetry problems of linguistic scale. A possible value range of the leverage factor is studied. A new linguistic conversation model is accordingly established with an unbalanced linguistic label and a cloud model. The authors develop a new online recommendation approach based on several modules, such as initialization, conversion, user-clustering and recommendation models.

The unbalanced effect between negative and positive reviews is verified with real data and measured using indirect methods. A new online recommendation approach of electronic products is proposed and used as an illustrative example to prove the practicality, effectiveness and feasibility of the proposed approach.

Due to the unavailable transaction information of customers, the limitation of this study is the effectiveness of the authors’ established recommendation system for platform or website cannot be verified.

In most existing studies, the influence of negative review is counterbalanced by positive review, and the unbalanced effect between negative and positive reviews is ignored. The negative review receives much attention from consumers and businesses. This study thus highlights the influence of negative review.

Aiming at the problem that quadruped crawling robot is easy to collide and overturn when facing obstacles and bulges in the process of complex slope movement, this paper aims to propose an obstacle avoidance gait planning of quadruped crawling robot based on slope terrain recognition.

First, considering the problem of low uniformity of feature points in terrain recognition images under complex slopes, which leads to too long feature point extraction time, an improved ORB (Oriented FAST and Rotated BRIEF) feature point extraction method is proposed; second, when the robot avoids obstacles or climbs over bumps, aiming at the problem that the robustness of a single step cannot satisfy the above two motions at the same time, the crawling gait is planned according to the complex slope terrain, and a robot obstacle avoidance gait planning based on the artificial potential field method is proposed. Finally, the slope walking experiment is carried out in the Robot Operating System.

The proposed method provides a solution for the efficient walking of robot under slope. The experimental results show that the extraction time of the improved ORB extraction algorithm is 12.61% less than the original ORB extraction algorithm. The vibration amplitude of the robot’s centroid motion curve is significantly reduced, and the contact force is reduced by 7.76%. The time it takes for the foot contact force to stabilize has been shortened by 0.25 s. This fact is verified by simulation and test.

The method proposed in this paper uses the improved feature point recognition algorithm and obstacle avoidance gait planning to realize the efficient walking of quadruped crawling robot on the slope. The walking stability of quadruped crawling robot is tested by prototype.

Because online shopping is risky, there is a strong need to develop better presentation of online reviews, which may reduce the perceived risk and create more pleasurable shopping experiences. To test the impact of online reviews’ sentiment polarity presentation, the purpose of this paper is to adopt a scenario experiment to study consumers’ decision-making process under the two scenarios of mixed presentation and classified presentation of online reviews collected from Jingdong.com in China: focusing on the comparative analysis on the differences of the consumers’ perceived risk, purchase intention and purchase delay, and further studying the interaction effect of involvement and online reviews’ sentiment polarity presentation.

This paper employed a 2×2 factorial experiment to test the hypothesis. The experimental design is divided into four groups: 2 (online reviews’ sentiment polarity presentation: mixed presentation vs classified presentation) × 2 (involvement: low vs high), each of which contains 90 samples. Through the data analysis, the main effect, mediation effect and moderating effect were examined.

The results show that compared with mixed presentation, classified presentation can reduce purchase intention and increase purchase delay due to the existence of loss aversion and availability heuristic. Furthermore, the paper also confirms that there is a significant interaction effect between involvement and online reviews’ sentiment polarity presentation.

The existing research pays less attention to the impact of online reviews presentation on consumers’ decision making, especially the lack of discussion on the interaction effect between involvement and online reviews presentation. For this reason, this paper proposes a problem, which concerns whether mixed presentation and classified presentation of online reviews will affect consumers’ decision making differently.

Despite concerns about the effect of internet addiction, little is known about how psychological suzhi impacts the internet addiction of college students. This paper aims to investigate the relationship between psychological suzhi and internet addiction among college students.

Using the college student psychological suzhi scale and internet addiction test, 2,070 college students from 11 universities in North China, East China, South China and Southwest China were tested.

The detection rate of internet addiction in this college sample of students was 18.8%. There was a significant negative correlation between students’ psychological suzhi and internet addiction (r = −0.408, p < 0.01). Hierarchical regression analysis showed that adaptability and individuality in psychological suzhi significantly negatively predicted college students’ internet addiction tendency (p < 0.001).

This study is the first to show a relationship between psychological suzhi and internet addiction in college students. In detail, the adaptability and individuality of college students’ psychological suzhi are protective factors related to internet addiction. The results also suggested that the authors can prevent and intervene in internet addiction by modifying college students’ adaptability and individuality.