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This paper aims to investigate the friction noise properties of M50 matrix curved microporous channel composites filled with solid lubricant Sn-Ag-Cu (MS).

Pure M50 (MA) and MS are prepared by selective laser melting and vacuum-pressure infiltration technology. The tribological and friction noise properties of MA and MS are tested through dry sliding friction and then the influential mechanism of surface wear sate on friction noise is investigated by analyzing the variation law of noise signals and the worn surface characteristics of MS.

Experimental results show that the friction noise sound pressure level of MS is only 75.6 dB, and it mainly consists of low-frequency noise. The Sn-Ag-Cu improves the surface wear state, which reduces self-excited vibration of the interface caused by fluctuation of friction force, leading to the decrease of friction noise.

This investigation is meaningful to improve the tribological property and suppress the friction noise of M50 bearing steel.

The purpose of this research is to investigate users’ expectations and preferences for transformable design functions of transformable party dresses to extend the life cycle of the clothing. With transformable design elements added to a party dress, a user can wear her one-event dress more than once, encouraging sustainable consumption. Rather than looking for a new dress, users can wear their party dress with one or even several new looks.

Data were collected through purposive sampling from female college students who had purchased at least one party dress. The survey questionnaire was self-developed based on the literature review and consisted of items that measured fashion behavior in relation to party dresses; shopping orientations, focusing on confident/appearance and fashion-consciousness, economic/price consciousness and convenience/time-consciousness; perceived importance of design and functional elements in party dresses; perceived reasons for the limited use of party dresses; perceived importance of design elements of transformable dresses; purchase intention for transformable dresses; and demographic characteristics.

As consumer groups of diverse shopping orientations demonstrated purchase intentions for transformable party dresses, transformable features may become an important component of party dresses. Considering the main reasons for users’ limited use of party dresses, designers need to improve the versatility of the dresses’ design and functional elements, focusing on style and occasion, dress length, size/fit, silhouette and color/pattern, so that users can wear their party dresses more than once and ultimately enhance their sustainable apparel consumption practices.

The findings regarding users’ expectations and preferences for transformable party dresses can help apparel designers and fashion businesses understand potential users when developing transformable dresses and develop strategies to help with sustainable apparel consumption.

Social network group decision-making (SNGDM) has rapidly developed because of the impact of social relationships on decision-making behavior. However, not only do social relationships affect decision-making behavior, but decision-making behavior also affects social relationships. Such complicated interactions are rarely considered in current research. To bridge this gap, this study proposes an SNGDM model that considers the interaction between social trust relationships and opinion evolution.

First, the trust propagation and aggregation operators are improved to obtain a complete social trust relationship among decision-makers (DMs). Second, the evolution of preference information under the influence of trust relationships is measured, and the development of trust relationships during consensus interactions is predicted. Finally, the iteration of consensus interactions is simulated using an opinion dynamics model. A case study is used to verify the feasibility of the proposed model.

The proposed model can predict consensus achievement based on a group’s initial trust relationship and preference information and effectively captures the dynamic characteristics of opinion evolution in social networks.

This study proposes an SNGDM model that considers the interaction of trust and opinion. The proposed model improves trust propagation and aggregation operators, determines improved preference information based on the existing trust relationships and predicts the evolution of trust relationships in the consensus process. The dynamic interaction between the two accelerates DMs to reach a consensus.

This study aims to investigate the following three issues: whether consumers process numeric information with locational cues, which locations (horizontal vs vertical) are more influential in processing numbers and whether a number-location association is weakened or strengthened when a visual reference frame moves up or down.

A field study and a series of three lab experiments were conducted to examine the location effect of numeric information on the package façade on the perceived magnitude of a number.

The authors found that a number at the right was perceived as larger than one at the left only when the number is located at the bottom. Also, placing numeric information at the bottom rather than the top of a product package façade was more powerful in processing the numeric information, but this is true only when the visual frame is set lower.

This study provides practical insights for product managers in placing core numeric information on product packaging to effectively communicate product value to consumers. Optimal locations can be deliberately considered along with types of numeric information and product categories. For healthy products that promote fewer calories, the top area of the package façade may be a better position for placing information on calories per serving to make the product more appealing to those who follow a healthy diet. Heavier, more voluminous products (e.g., refrigerator) better position their volume/weight information at the bottom than at the top or at the right of the bottom than at the left of the bottom on the product facade. Either the left side or right side of the top position may be beneficial for thinner, lightweight products (e.g., television).

The present work adds valuable empirical findings; inconsistent with past research, left-right location-number associations are not always true. People tend to associate smaller numbers with left-side locations and larger numbers with right-side locations only when the number is located at the bottom. Also, the study reported that top-small, bottom-large associations are not always true. The difference in perceived magnitude of the number between a number at the top and one at the bottom within the visual frame is significant only when the visual frame is set close to the ground.

The purpose of this paper is to maximize the load-carrying capacity (LCC) of a rotating ring, a numerical model optimizing both the surface and bottom shape of its surface textures is proposed.

The Reynolds equation is used to evaluate the film pressure and LCC obtained by integrating the film pressure is set as the objective function. Around the center of the computational domain, radial lines with an equal angle between adjacent ones are produced and the surface contour of textures is obtained by connecting the endpoints using a spline curve. The bottom profile is then obtained by connecting the endpoints of two vertical lines at the circumferential ends of textures. Lengths of these lines are set as design variables and genetic algorithm is used to solve optimization models.

Results show that optimum textures have an “apple-like” surface contour and a “wedge-like” bottom profile, which are both expressed by smooth spline curves. Optimum wedge-bottom textures generate higher LCC than optimum flat-bottom textures. Moreover, the optimum textures have the highest LCC compared with optimum grooves proposed previously, which validates the practical value of the current optimization model.

This work presents a comprehensive optimization method of texture geometry, which provides a new idea of the design of surface textures.

This paper aims to investigate the temporal dynamics of users browsing activity on a hotel website in order to derive effective marketing strategies and constantly improve website effectiveness. Users' activities on the hotel's website on yearly, monthly, daily and hourly basis are examined and compared, demonstrating the power of informatics and data analytics.

A total of 29,976 hourly Weblog files from 1 August 2014 to 31 December 2017 were collected from a luxury hotel in Hong Kong. ANOVA and post-hoc comparisons were used to analyse the data.

Users' browsing behaviours, particularly stickiness, on the hotel website differ on yearly, monthly, daily and weekly bases. Users' activities increased steadily from 2014 to 2016, but dropped in 2017. Users are most active from July to September, on weekdays, and from noon to evening time. The month-, day-, and hour-based behaviours changed through years. The analysis of big data determines strategic and operational management and marketing decision-making.

Understanding the usage patterns of their websites allow organisations to make a range of strategic, marketing, pricing and distribution decisions to optimise their performance. Fluctuation of website usage and level of customer engagement have implications on customer support and services, as well as strategic partnership decisions.

Leveraging the power of big data analytics, this paper adds to the existing literature by performing a comprehensive analysis on the temporal dynamics of users' online browsing behaviours.

The purpose of this chapter is to optimize multi-criteria formulation for green vehicle routing problems by mixed integer programming. This research is about the road freight transportation of a Spanish company of groceries. This company has more power in the north of Spain and hence it was founded there. The data used for the computational experiments are focused in the northern region of Spain. The data have been used to decide the best route in order to obtain a minimization of costs for the company. The problem focused on the distance traveled and the altitude difference; by studying these parameters, the best solution of route transportation has been made. The software used to solve this model is CPLEX solver with AMPL programming language. This has been helpful to obtain the results for the research and some conclusions have been obtained from them.

Considering the impact of the Free Trade Zone (FTZ) policy on forecasting the port cargo throughput, this paper constructs a fractional grey multivariate forecasting model to improve the prediction accuracy of port cargo throughput and realize the coordinated development of FTZ policymaking and port construction.

Considering the effects of data randomization, this paper proposes a novel self-adaptive grey multivariate prediction model, namely FDCGM(1,N). First, fractional-order accumulative generation operation (AGO) is introduced, which integrates the policy impact effect. Second, the heuristic grey wolf optimization (GWO) algorithm is used to determine the optimal nonlinear parameters. Finally, the novel model is then applied to port scale simulation and forecasting in Tianjin and Fujian where FTZs are situated and compared with three other grey models and two machine learning models.

In the Tianjin and Fujian cases, the new model outperforms the other comparison models, with the least mean absolute percentage error (MAPE) values of 6.07% and 4.16% in the simulation phase, and 6.70% and 1.63% in the forecasting phase, respectively. The results of the comparative analysis find that after the constitution of the FTZs, Tianjin’s port cargo throughput has shown a slow growth trend, and Fujian’s port cargo throughput has exhibited rapid growth. Further, the port cargo throughput of Tianjin and Fujian will maintain a growing trend in the next four years.

The new multivariable grey model can effectively reduce the impact of data randomness on forecasting. Meanwhile, FTZ policy has regional heterogeneity in port development, and the government can take different measures to improve the development of ports.

Under the background of FTZ policy, the new multivariable model can be used to achieve accurate prediction, which is conducive to determining the direction of port development and planning the port layout.

This study aims to investigate the users’ perception on the utilisation of artificial intelligence (AI) for the management of records at the Council for Scientific and Industrial Research (CSIR) in South Africa. User perception plays a crucial role in the utilisation of AI for the management of records at the CSIR. It is important to know the views of the users, especially how they think AI can be used for effective and efficient management of records.

The convergent mixed methods research was applied, and data was collected using interviews and questionnaires. Data was analysed thematically and statistically and presented using tables and figures.

This study reveals that the users were not aware of the application of AI for the management of records until the workshops, which were facilitated by the researcher. The users are of the view that AI can be used to provide efficient storage of records, quick retrieval of records and adequate security. This study further reveals that the CSIR is not yet ready to use AI for the management of records because of the lack of knowledge and resources to implement AI.

This study also proposes a framework regarding the users’ perception on the utilisation of AI for the management of records at the CSIR. It is hoped that the framework proposed will serve as a benchmark and guideline for user perception regarding the use of AI for the management of records in the archives and records management industry.

Farm income in the agricultural sector is susceptible to natural and market risks. A large body of literature has studied the effects of cooperative membership on household welfare, technical efficiency, productivity and production behavior, yet little has been known about the impact of cooperative membership on farm income volatility. This paper aims to fill this research gap by investigating the relationship between cooperative membership and farm income volatility of Chinese pig farmers and drawing policy implications.

This paper examines the effect of cooperative membership on farm income volatility, using data from a two-round survey of pig farmers in China. The authors employ an endogenous switching regression model to address the selection bias issues associated with unobserved factors simultaneously affecting farmers' participation in agricultural cooperatives and income earning activities.

Using household panel from a two-round survey of 193 pig farmers in China, this analysis highlights two key findings: (1) agricultural cooperative membership has significant and positive effect on farm income stability and (2) the impact of cooperative membership on farm income stability varies with production scale.

This research makes two contributions to the literature. First, this study contributes to the scant literature exploring the relationship between agricultural cooperatives and farm income stability. Second, to the best of the authors' knowledge, this is the first study that explores such relationship in a livestock sector. The pig sector in China and around the developing world has been increasingly challenged by multifaceted risks (e.g. price fluctuations, epidemic diseases, environmental regulations), and understanding the role of agricultural cooperatives in farm income stability of pig farmers is of great practical and policy significance.