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The purpose of this paper is to analyze the stiffness reliability of harmonic drive (HD) considering contact pairs wear.

In terms of theoretical calculation, the contact pairs wear of HD are calculated based on Archard wear formula and the relative motion characteristics of contact pairs. According to the motion trajectory of flexspline teeth, the teeth backlash and the number of meshing teeth, the meshing stiffness and overall stiffness of HD are analyzed considering the wear and its randomness of contact pairs. Combined with Monte Carlo Simulation, the stiffness reliability evaluation method considering contact pairs wear is proposed, and the result of this method is verified by the stiffness reliability result deduced from the stiffness degradation measurement data.

Considering contact pairs wear, during operation, the teeth backlash increases, the number of meshing teeth decreases, the meshing stiffness decreases, ultimately leading to a gradual decrease in the overall stiffness of HD. When only one type of contact pair wear is considered, the influence of flexspline and circular spline contact pair wear on HD stiffness reliability is greater. Compared with the stiffness reliability evaluation results obtained from the stiffness degradation data in the literature, the mathematic expectation of stiffness degradation failure life distribution obtained from the proposed method is relatively bigger.

The stiffness reliability evaluation method of HD considering contact pairs wear is firstly proposed. The stiffness reliability evaluation result from theoretical calculation is verified by the stiffness reliability results deduced from HD stiffness degradation measurement.

The purpose of this paper is to present an oligopolistic version of the cobweb model that departs from the strict assumptions of perfect competition in the traditional cobweb model.

Introducing a model where n identical producers engage in Cournot competition, with output decisions influencing market prices. The paper retains the original assumptions of naive expectations and a linear model where price expectations of Cournot competitors are made simultaneously with production decisions. The investigation focuses on the model's behavior as the number of producers decreases or industry concentration increases. The authors also show empirical evidence when drawing the data from the pig sector in China and the USA.

The findings indicate that the cobweb model undergoes a transition from divergent to continuous and even convergent as the number of producers decreases or industry concentration increases. The incorporation of costs related to entry and exit from the market contributes to achieving a more stable equilibrium state.

The cobweb model has been primarily studied in an idealized market structure of perfect competition, and the assumptions that they share are not obviously appropriate to many agriculture markets. This study presents an alternative version of the cobweb model in an oligopolistic market that relaxes the strict assumptions of perfect competition. The authors show the dynamics of reduced competitor numbers or increased industry concentration on the convergence of the cobweb model based on subtle variations in parameters.

China has been going through a “museum boom” paralleling the domestic tourism boom since 2000; such growth changed the cultural landscape; museums became a vital characteristic of some Chinese cities for both residents and tourists. Encouraged by this growth, the more ambitious “All-for-one Museum (全域博物馆)” was proposed. The physical boundary between museums and living spaces is infinite ambiguity, challenging the idea of museums as “heterotopias.” This study aims to explore the musealization of urban spaces in the context of anthropology and museology, scrutinizing the cultural-political intentions and meanings of these developments, and seeks to ignite further investigation into the reconstruction of historical imaginaries for tourists and urban populations across related disciplines.

This paper examines two cases in Chinese metropolises, Beijing and Shanghai, to illustrate this development of musealization, that is, how the cities actively leverage museological values and methods to connect with their past. In the Beijing case, the authors explore how the local government is leading the effort to musealize the city; in the Shanghai case, they will see how tourists, especially dweller-tourists, navigate through a curated past story in the city and connect their own experience, memory and identity with the place.

The all-for-one museum creates a museal layer projected onto the bigger urban space, even though the authenticity of the “past” is challenged by the modernization development of the city. The authors also find out that for some tourists (especially dweller-tourists), an existential sense of authenticity plays a more significant role as they not only seek to sightsee the past of the city but also to take part in its creation.

This paper discusses two kinds of musealization in cosmopolitan cities of Beijing and Shanghai: top-down and bottom-up. It approaches questions about the musealization of urban spaces from the perspectives of anthropology and museology, and discusses musealization in the specific historical context of China’s modernization process.

This paper aims to provide a new method to study and improve the dynamic characteristics of the four-column resistance strain force sensor through the elastomer structure design and optimization.

Based on the mechanism analysis method, the authors first present a dynamic characteristic model of the four-column resistance strain force sensors’ elastomer. Then, the authors verified and modified the model according to the Solidworks finite element simulation results. Finally, the authors designed and optimized two types of four-column elastomers based on the dynamic characteristic model and verified the improvement of sensor dynamic performance through a hammer knock dynamic experiment.

The Solidworks finite element simulation and hammer knock dynamic experiment results show that the relative error of the model is less than 10%, which confirms the accuracy of the model. The dynamic performance of the sensors based on the model can be improved by more than 30%, which is a great improvement in sensor dynamic performance.

The authors first present a dynamic characteristic model of the four-column elastomer and optimize the four-column sensors successfully based on the mechanism analysis method. And a new method to study and improve the dynamic characteristics of the resistance is provided.

The collaborative filtering algorithm is a classical and widely used approach in product recommendation systems. However, the existing algorithms rely mostly on common ratings of items and do not consider temporal information about items or user interests. To solve this problem, this study proposes a new user-item composite filtering (UICF) recommendation framework by leveraging temporal semantics.

The UICF framework fully utilizes the time information of item ratings for measuring the similarity of items and takes into account the short-term and long-term interest decay for computing users’ latest interest degrees. For an item to be probably recommended to a user, the interest degrees of the user on all the historically rated items are weighted by their similarities with the item to be recommended and then added up to predict the recommendation degree.

Comprehensive experiments on the MovieLens and KuaiRec datasets for user movie recommendation were conducted to evaluate the performance of the proposed UICF framework. Experimental results show that the UICF outperformed three well-known recommendation algorithms Item-Based Collaborative Filtering (IBCF), User-Based Collaborative Filtering (UBCF) and User-Popularity Composite Filtering (UPCF) in the root mean square error (RMSE), mean absolute error (MAE) and F1 metrics, especially yielding an average decrease of 11.9% in MAE.

A UICF recommendation framework is proposed that combines a time-aware item similarity model and a time-wise user interest degree model. It overcomes the limitations of common rating items and utilizes temporal information in item ratings and user interests effectively, resulting in more accurate and personalized recommendations.

This study aimed to investigate whether information from reliable news sources such as medical experts and government officials, along with governmental and individual risk responses, influences consumers’ perceptions of news and intention to seek more information. Additionally, it aimed to explore the relationships between these perceptions and consumers’ intentions to seek information in a food safety risk event.

A survey design methodology was employed. A quasi-experimental approach divided 470 Taiwanese participants into three groups, each exposed to varying online news content about food safety news, designed according to the Internalization-Distribution-Explanation-Action (IDEA) model. This involved different combinations of reliable sources and risk response advice to examine the impact on news comprehension and behaviour intentions.

The results indicated that consumers perceived the news as highly credible when they read it with reliable news sources or risk response advice. Governmental and individual risk response advice significantly impacted consumers’ understanding of news. In addition, perceptions of news credibility and understanding of news can increase individuals’ information-seeking intentions to protect themselves from food safety risks.

This study introduced novel insights into the application of the source credibility theory (SCT) model within Taiwanese food safety incidents, identifying key factors that motivate consumer information-seeking behaviour. It marks an initial attempt to incorporate the IDEA model-based risk communication content into research design, aligning with existing literature while highlighting the critical role of reliable sources in enhancing news credibility and consumer response.

A cross-domain intelligent software-defined network (SDN) routing method based on a proposed multiagent deep reinforcement learning (MDRL) method is developed.

First, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between the root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to acquire global network state information in real time. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a network traffic state prediction mechanism is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time.

Experimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and open shortest path first (OSPF) routing methods.

Message transmission and message synchronization for multicontroller interdomain routing in SDN have long adaptation times and slow convergence speeds, coupled with the shortcomings of traditional interdomain routing methods, such as cumbersome configuration and inflexible acquisition of network state information. These drawbacks make it difficult to obtain global state information about the network, and the optimal routing decision cannot be made in real time, affecting network performance. This paper proposes a cross-domain intelligent SDN routing method based on a proposed MDRL method. First, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to realize the real-time acquisition of global network state information. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a prediction mechanism for the network traffic state is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time. Experimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and OSPF routing methods.

This paper aims to prepare third-order nonlinear optical (NLO) organic materials with large nonlinear optimization value, high damage threshold and ultrafast response time.

A series of novel symmetric and asymmetric compounds possessing third-order NLO properties were synthesized using 1,3,5-tribromobenzene as the basis. The photophysical and electrochemical properties, as well as the click reactions, were characterized by means of UV–VIS–NIR absorption spectroscopy and cyclic voltammetry.

The donor–acceptor chromophores were inserted into compound, making the molecule to have a broader absorption in the near-infrared regions and a narrower optical and electrochemical band gap. It also formed an electron-delocalized organic system, which has larger effects on achieving a third-order NLO response. The third-order NLO phenomenon of benzene ring complexes was experimentally studied at 532 nm using Z-scan technology, and some compounds showed the expected NLO properties.

The click products exhibit more NLO phenomena by performing different click combinations to the side groups, opening new perspectives on using the system in a variety of photoelectric applications.

The purpose of this paper is to propose a scenario-based grey methodology using clustering and optimizing with imprecise and uncertain body size data in an emergency assembly point area to assign the people on a campus to reach the emergency assembly points under uncertain disaster times.

Grey clustering and a new grey p-median linear programming model are developed to determine which units to assign to the pre-determined assembly points for a main campus in case of a disaster. The models have two scenarios: 70 and 100% occurrence capacities of administrative and academic personnel and students.

In this study, the academic and administrative units have been assigned to determine five different emergency assembly points on the main campus by using the numbers of the academic and administrative personnel and student and distances of the units to the assembly point areas of each other. The alternative solutions are obtained effectively by evaluating capacity utilization rates in the scenarios.

It is often unclear when disasters can occur and therefore, a preliminary preparation time must be required to minimize the risk. In the case of natural, man-made (unnatural) or technological disasters, the people are required to defend themselves and move away from the disaster area as soon as possible in a proper direction. The proposed assignment model yields a final solution that effectively eliminates uncertainty regarding the selection of emergency assembly points for administrative and academic staff as well as students, in the event of disasters.

Grey clustering suggests an assignment plan and concurrently, an investigation is underway utilizing the grey p-median linear programming model. This investigation aims to optimize various scenarios and body sizes concerning emergency assembly areas. All campus users who are present at the disaster in units of the campus are getting uncertainty about which emergency assembly point to use, and with this study, the vital risks aim to be ultimately reduced with reasonable plans.

Based on the Stimulus-Organism-Response Model, this study aims to investigate how the intention of Chinese guests to revisit a hotel (response) is triggered by the quality of the hotel’s hygiene protocols (stimulus) during the pandemic. Brand image, perceived guest satisfaction and perceived customer trust were examined as the organism factors in this model.

The quantitative method was adopted to collect data via a structured online survey of 385 Chinese hotel guests. Their responses were analyzed using SPSS (v.26) and SmartPLS (3.3.2) software.

The quality of hotel hygiene protocols was found to have a significant impact on hotel brand image, perceived guest satisfaction and perceived guest trust. Hotel brand image, perceived guest satisfaction and perceived guest trust, in turn, demonstrated significant relationships with guests’ revisit intention.

Theoretically, the present study offers a framework to understand the impact of hotel hygiene protocols on guest revisit intention. Practically, the findings of the study encourage industry practitioners to implement proper safety protocols and standard operating procedures related to COVID-19.

Since the beginning of the pandemic, hotel hygiene standards have become a key concern for guests. The current study provides important and meaningful insights into whether and how hotel hygiene quality promotes guest revisit intention.