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The skin and skeleton of aircraft are connected by adhesives or rivets to bear and transfer aerodynamic load. It is easy for crack and fracture damage to occur under the action of cyclic load, thus reducing aircraft bearing capacity/integrity and causing serious security risks. Therefore, it is particularly important that passive wireless radio frequency identification (RFID) sensors be used for the health monitoring of aircraft skin in its whole life cycle. This paper aims to investigate the influence of miniaturization on the coupling effect between RFID tag sensors.

Two groups of crack sensing systems based on RFID tags were designed. Gain and mutual impedance of sensor tags were analyzed via mode analysis. The reliability of crack detection of both sensing systems was compared using a preset experimental scheme.

Miniaturized antennas can reduce edge influence and the coupling effect. Gain and mutual impedance decrease with the increase in distance between dual tags. Backscatter power shows a decreasing trend and threshold power to activate tags in reader antenna increases. Results show that the miniaturization of size is more suitable for the application of multiple sensors.

By comparing two groups of sensing systems, the consistency of crack detection sensitivity is better when small tags are placed in parallel, which provides a theoretical basis for the application of small, passive and densely distributed crack sensors in the future.

The purpose of this paper is to analyse the impacts of storage losses and market development on the maize-selling behaviours of rural households in China.

Based on the survey data of 543 households from nine major maize production provinces in China, the authors introduce storage losses to a household's maize-selling decision-making model and use fractional logit model and ordered probit model to empirically analyse the impact of maize storage losses and market development on household maize-selling decisions in China. To overcome potential endogeneity problems, the authors select the weather at drying (whether bad weather occurs during the drying process) and harvest loss as instrumental variables and re-estimate the model.

The results show that increased storage losses prompt farmers to increase the proportion of maize sold within three months after harvest and sell maize in advance. Meanwhile, the degree of market development has a significant impact on farmers' maize-selling decisions. Other factors, such as the maize output, non-agricultural employment and awareness of loss control, also affect farmers' maize-selling behaviours.

The government should promote advanced storage facilities, reduce household storage losses, decrease the phenomenon of centralised sales after harvest and help farmers freely choose the suitable time for sales. The government also needs to strengthen market information releases and publicity, reduce transaction costs and help farmers make reasonable sales decisions.

The authors introduce storage losses as a separate variable in a farmer's grain-selling decision model to empirically analyse the impact of storage losses on farmers' grain-selling behaviours. Moreover, the authors analyse the impact of market development on household grain-selling behaviours in China. These findings can help avoid oversupply in the market during the harvest season and alleviate the pressure on the market from the supply and demand imbalance. These results are also beneficial for farmers waiting for a higher price and increasing their income.

The purpose of this study was to estimate on-farm maize storage losses and to empirically analyse the main determinants of maize storage losses in China.

Based on a nationwide survey of 1,196 households in 23 provinces (autonomous regions and municipalities) in China, the authors estimated on-farm maize storage losses and used a fractional logit model to analyse the main determinants of maize storage losses in China.

The results showed that, first, 1.78% of maize was lost during the storage stage in China and that storage loss experienced by smallholders varied across regions. Second, the empirical analysis showed that storage quantities have significant and negative effects on storage losses and that an economy of scale in household storage may exist; the percentage of maize stored for consumption and feed has significant and positive effects on storage losses, which may be related to market requirements and management activities. Third, compared to traditional facilities, silos and warehouses cause lower storage losses, while spraying chemicals and re-drying maize in the storage stage incur higher losses, possibly because to save costs, smallholders conduct loss-reduction activities only when they suffer serious losses, and when taking measures, farmers may sort grains, which also increases losses. Fourth, harvesting maize when it matures is significantly associated with lower storage losses.

To reduce storage losses, first, farmers' awareness of food saving and loss reduction must be increased. This could be achieved through agricultural training and education regarding food-saving practices. Second, the government could provide subsidies or low-interest credits to encourage farmers to use advanced facilities and promote land transfers to realize economies of scale. Finally, the government should accelerate the construction of smart agriculture systems and the timely monitoring of crop growth to suggest farmers to harvest at the appropriate time.

Reducing post-harvest loss (PHL) has become an important means of increasing the food supply and reducing resources use in China. This study provides a complete introduction to household maize storage losses and can therefore help characterize the current state of PHL in China, which is of vital importance to food security and food policy.

Realistic force sensation can help operators better feel and manipulate parts for virtual assembly (VA). Moreover, for VA of mechanical parts, it is necessary to consider their tolerance levels so as to apply proper assembly forces. Out of the three common assembly fit types, the type of clearance fit is the focus of virtual manual assembly, as parts with such fit type require precise force feedback to assist users’ assembly operations.

This study proposes a novel force rendering model for VA of mechanical parts with clearance fits. By decomposing an actual assembly operation into three consecutive states, the corresponding forces are formulated.

A prototype system is designed and developed to implement the model, and comparative case studies are conducted to investigate the users’ performance with the other three common approaches, namely, a typical WIMP (window-icon-menu-pointer) interface with CAD software, a physics simulation with collision detection and the approach that combines physics simulation and geometric constraints restriction. The results have shown that the proposed model is more realistic by providing continuous and realistic force feedback to the users.

The users’ feeling of immersion and their operational efficiency are greatly enhanced with the force sensation provided.

In recent years, the retail landscape has undergone a seismic shift, transforming into a dynamic, interconnected ecosystem where consumers seamlessly traverse physical and digital realms. This shift has given rise to the omnipresent force known as Omni-channel retailing. This chapter aims to delve into the intricacies of Omni-channel retail strategies, exploring how businesses can leverage this paradigm to enhance customer experience, streamline operations, and drive sustainable growth. Omni-channel retail strategies encompass a range of approaches aimed at providing customers with a seamless and integrated shopping experience across various channels. These strategies can be categorized into several key areas, each involving specific modern technologies to enhance customer engagement and satisfaction.

This paper aims to provide a predictive framework of customer churn through six stages for accurate prediction and preventing customer churn in the field of business.

The six stages are as follows: first, collection of customer behavioral data and preparation of the data; second, the formation of derived variables and selection of influential variables, using a method of discriminant analysis; third, selection of training and testing data and reviewing their proportion; fourth, the development of prediction models using simple, bagging and boosting versions of supervised machine learning; fifth, comparison of churn prediction models based on different versions of machine-learning methods and selected variables; and sixth, providing appropriate strategies based on the proposed model.

According to the results, five variables, the number of items, reception of returned items, the discount, the distribution time and the prize beside the recency, frequency and monetary (RFM) variables (RFMITSDP), were chosen as the best predictor variables. The proposed model with accuracy of 97.92 per cent, in comparison to RFM, had much better performance in churn prediction and among the supervised machine learning methods, artificial neural network (ANN) had the highest accuracy, and decision trees (DT) was the least accurate one. The results show the substantially superiority of boosting versions in prediction compared with simple and bagging models.

The period of the available data was limited to two years. The research data were limited to only one grocery store whereby it may not be applicable to other industries; therefore, generalizing the results to other business centers should be used with caution.

Business owners must try to enforce a clear rule to provide a prize for a certain number of purchased items. Of course, the prize can be something other than the purchased item. Business owners must accept the items returned by the customers for any reasons, and the conditions for accepting returned items and the deadline for accepting the returned items must be clearly communicated to the customers. Store owners must consider a discount for a certain amount of purchase from the store. They have to use an exponential rule to increase the discount when the amount of purchase is increased to encourage customers for more purchase. The managers of large stores must try to quickly deliver the ordered items, and they should use equipped and new transporting vehicles and skilled and friendly workforce for delivering the items. It is recommended that the types of services, the rules for prizes, the discount, the rules for accepting the returned items and the method of distributing the items must be prepared and shown in the store for all the customers to see. The special services and reward rules of the store must be communicated to the customers using new media such as social networks. To predict the customer behaviors based on the data, the future researchers should use the boosting method because it increases efficiency and accuracy of prediction. It is recommended that for predicting the customer behaviors, particularly their churning status, the ANN method be used. To extract and select the important and effective variables influencing customer behaviors, the discriminant analysis method can be used which is a very accurate and powerful method for predicting the classes of the customers.

The current study tries to fill this gap by considering five basic and important variables besides RFM in stores, i.e. prize, discount, accepting returns, delay in distribution and the number of items, so that the business owners can understand the role services such as prizes, discount, distribution and accepting returns play in retraining the customers and preventing them from churning. Another innovation of the current study is the comparison of machine-learning methods with their boosting and bagging versions, especially considering the fact that previous studies do not consider the bagging method. The other reason for the study is the conflicting results regarding the superiority of machine-learning methods in a more accurate prediction of customer behaviors, including churning. For example, some studies introduce ANN (Huang et al., 2010; Hung and Wang, 2004; Keramati et al., 2014; Runge et al., 2014), some introduce support vector machine ( Guo-en and Wei-dong, 2008; Vafeiadis et al., 2015; Yu et al., 2011) and some introduce DT (Freund and Schapire, 1996; Qureshi et al., 2013; Umayaparvathi and Iyakutti, 2012) as the best predictor, confusing the users of the results of these studies regarding the best prediction method. The current study identifies the best prediction method specifically in the field of store businesses for researchers and the owners. Moreover, another innovation of the current study is using discriminant analysis for selecting and filtering variables which are important and effective in predicting churners and non-churners, which is not used in previous studies. Therefore, the current study is unique considering the used variables, the method of comparing their accuracy and the method of selecting effective variables.

This paper aims to explore the effects of normative and hedonic motivations on continuous knowledge contribution, and how past contribution experience moderates the effects of the motivations on continuous knowledge contribution.

Based on goal-framing theory, the present study proposes a comprehensive theoretical model by integrating normative and hedonic motivations, past contribution experience and continuous knowledge contribution. The data for virtual community members' activities were collected using the Python Scrapy crawler. Logit regression was used to validate the integrative model.

The results show that both normative motivation (reflected by generalized reciprocity and social learning) and hedonic motivation (reflected by peer recognition and online attractiveness) are positively associated with continuous knowledge contribution. Moreover, these effects are found to be significantly influenced by members' past knowledge contribution experience. Specifically, the results suggest that past knowledge contribution experience undermines the influence of generalized reciprocity on continuous knowledge contribution but strengthens the effect of peer recognition and online attractiveness.

Although the emerging literature on continuous knowledge contribution mainly focuses on motivations as antecedents that promote continuous knowledge contribution, most of these studies assume that the relationship between motivating mechanisms and continuous knowledge contribution does not change over time. The study is one of the initial studies to examine whether and how the influence of multiple motivations evolves relative to levels of past contribution experience.

Purpose – Intersections are hazardous locations and to improve their safety we need to understand the factors contributing to crashes at these locations and provide evidence-based recommendations to reduce them. This chapter provides a summary of the findings on infrastructure-related factors contributing to crashes at urban and rural intersections and some discussions on the implications and potential countermeasures.

Approach – A review of the literature on intersection crashes was performed to identify the infrastructure-related crash-contributing factors. Some discussions on the implications and potential countermeasures are then provided.

Findings – The factors contributing to road crashes are diverse and complex. While the safety effects of a few factors (e.g., exposure and speed) are relatively consistent, many factors have different impacts on crash frequency and severity (e.g., types of intersection) and different impacts on urban and rural intersections (e.g., bus stops).

Research Implications – More studies are needed on developing a stronger theoretical or conceptual foundation on the effects of roadway designs and traffic controls on different dimensions of safety (e.g., exposure, frequency, severity, etc.), types of crashes (e.g., head-on, rear-end, etc.) or road users involved (e.g., drivers, pedestrians, cyclists, etc.).

Practical Implications – Transport engineers need to be aware that some treatments may have different effects on different crash types and road users involved. Even though the overall safety may be improved by the treatments designed, they need to consider and mitigate any unintended consequences to satisfy the Pareto improvement principle and the social equity criterion.

This study examines the impact of incidental pride on consumer preference for attention-grabbing products. This effect is mediated by the desire to gain attention. This study also shows that the effect of incidental pride is qualified by visibility of consumption.

Using two studies with between-subjects designs, this research examines the difference in preferences for attention-grabbing products between hubristic and authentic pride.

Individuals who experience hubristic pride (vs authentic pride) show greater preference for attention-grabbing products and have a strong desire to gain attention from others. However, when consumption is perceived as private (vs public), preferences for attention-grabbing products weaken for those who experience hubristic pride.

This research studies the effect of incidental pride on consumer preference. By examining dispositional pride effects, future research may expand these findings, which enrich the literature on emotion. Future research can identify the potential mechanism for the relationship between authentic pride and preference for attention-grabbing products in the context of private consumption.

Marketers and salespersons can guide and recommend products with attention-grabbing features to customers celebrating a friend’s success in recognition of their innate ability. Second, marketers may encourage consumers to buy attention-grabbing products with targeted advertising or emotion-eliciting advertising (i.e., evoke a certain type of pride).

While prior studies focused on basic emotions, this research has investigated self-conscious emotions that are central to consumer behavior. This research contributes to the understanding of self-conscious emotions that affect consumers’ behavioral responses in unrelated situations. Investigating the two facets of pride, the findings show the impact of pride on the preference for attention-grabbing products and reveals that visibility of consumption moderates the effect of pride.

The purpose of this paper is to identify the historical trends and status of the national development of artificial intelligence (AI) from a nationwide perspective and to enable governments at different administrative levels to promote AI development through policymaking.

This paper analyzed 248 Chinese AI policies (36 issued by the state agencies and 212 by the regional agencies). Policy bibliometrics, policy instruments and network analysis were used to reveal the AI policy patterns. Three aspects were analyzed: the spatiotemporal distribution of issued policies, the policy foci and instruments of policy contents and the cooperation and citation among policy-issuing agencies.

Results indicate that Chinese AI development is still in the initial phase. During the policymaking processes, the state and regional policy foci have strong consistency; however, the coordination among state and regional agencies is supposed to be strengthened. According to the issuing time of AI policies, Chinese AI development is in accordance with the global situation and has witnessed unprecedented growth in the last five years. And the coastal provinces have issued more targeted policies than the middle and western provinces. Governments at the state and regional levels have emphasized familiar policy foci and played the role of policymakers, along with regional governments that also functioned as policy executors as well. According to the three-dimension instruments coding, the authors found an uneven structure of policy instruments at both levels. Furthermore, weak cooperation appears at the state level, while little cooperation is found among regional agencies. Regional governments cite state policies, thus leading to the formation of top-down diffusion, lacking bottom-up diffusion.

The paper contributes to the literature by characterizing policy patterns from both external attributes and semantic contents, thus revealing features of policy distribution, contents and agencies. What is more, this research analyzes Chinese AI policies from a nationwide perspective, which contributes to clarifying the overall status and multi-level relationships of policies. The findings also benefit the coordinated development of governments during further policymaking processes.