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This paper aims to design an algorithm which is used to deal with non-linear discrete systems with constraints under the lower computation burden. As a result, we solve the non-holonomic vehicle tracking problem with the lower computational load and the convergence performance.

A fusion event-triggered model predictive control version is developed in this paper. The authors designed a shrinking prediction strategy.

The fusion event-triggered model predictive control scheme combines the strong points of event triggered and self-triggered methods. As the practical state approaches the terminal set, the computational complexity of optimal control problem (OCP) decreases.

The proposed strategy has proven to stabilize the system and also guarantee a reproducible solution for the OCP. Also, it is proved to be effected by the performance of the simulation results.

This paper aims to develop an integrative framework based on a convergence of embodiment, ecological and phenomenological theoretical perspectives to explain the multiple processes involved in the consumers’ mining, processing and application of brand-related sensory data through a sensory brand experience (SBE).

This research adopts a qualitative method by using face-to-face in-depth interviews (retail managers and customers) and focus group interviews (actual customers) with 34 respondents to investigate SBEs in the context of Chinese shopping malls.

Results show that the brand data mined through multisensory cues (visual, auditory, olfactory, tactile and taste) in a brand setting are processed internally as SBEs (involving sensory impressions, fun, interesting, extraordinary, comforting, caring, innovative, pleasant, appealing and convenient), which influence key variables in customer–brand relationships including customer satisfaction, brand attachment and customer lovemarks.

This study has implications for current theory on experiential marketing, branding, consumer–brand relationships, consumer psychology and customer experience management.

The purpose of this paper is to analyse consumer purchasing behaviour in different cultural settings by exploring the value of consumer reviews from various countries.

This study uses online review mining technology to collect, process and analyse user review data from multiple countries. The main procedures of this research are data collection, data pre-processing, feature extraction and sentiment analysis. Online reviews from the American, British and Indian websites for the iPhone 5s are analysed.

Every country has unique cultural characteristics, and these cultural differences affect consumers’ perceptions, attitudes and purchasing behaviours. The results show that consumers from different countries exhibit different levels of attention towards the same product and have different emotional inclinations for the same product feature. In addition, the study also identified the advantages and disadvantages of the product.

The user reviews provide abundant feedback information that serves as a good intelligence resource for companies. Under the premise of different language habits, this paper uses a universal approach to analyse consumer behaviour from online reviews in different countries, which can help reveal consumers’ emotional inclination towards each feature of a product. This approach can be extended to other brands of mobile phones or other industries.

Multinational companies should analyse the cultural characteristics of target groups when proposing transnational development strategies. Companies can understand the perceptions of their products based on the consumer reviews and can formulate their marketing and product strategies by considering consumer purchasing behaviours arising from cultural differences.

This study identifies differences in consumer behaviour in different cultural settings by using a data mining method, which can help companies understand consumer perceptions and the performance and quality of product features.

Density-based spatial clustering of applications with noise (DBSCAN) is the most commonly used density-based clustering algorithm, while it cannot be directly applied to the railway investment risk assessment. To overcome the shortcomings of calculation method and parameter limits of DBSCAN, this paper proposes a new algorithm called Improved Multiple Density-based Spatial clustering of Applications with Noise (IM-DBSCAN) based on the DBSCAN and rough set theory.

First, the authors develop an improved affinity propagation (AP) algorithm, which is then combined with the DBSCAN (hereinafter referred to as AP-DBSCAN for short) to improve the parameter setting and efficiency of the DBSCAN. Second, the IM-DBSCAN algorithm, which consists of the AP-DBSCAN and a modified rough set, is designed to investigate the railway investment risk. Finally, the IM-DBSCAN algorithm is tested on the China–Laos railway's investment risk assessment, and its performance is compared with other related algorithms.

The IM-DBSCAN algorithm is implemented on China–Laos railway's investment risk assessment and compares with other related algorithms. The clustering results validate that the AP-DBSCAN algorithm is feasible and efficient in terms of clustering accuracy and operating time. In addition, the experimental results also indicate that the IM-DBSCAN algorithm can be used as an effective method for the prospective risk assessment in railway investment.

This study proposes IM-DBSCAN algorithm that consists of the AP-DBSCAN and a modified rough set to study the railway investment risk. Different from the existing clustering algorithms, AP-DBSCAN put forward the density calculation method to simplify the process of optimizing DBSCAN parameters. Instead of using Euclidean distance approach, the cutoff distance method is introduced to improve the similarity measure for optimizing the parameters. The developed AP-DBSCAN is used to classify the China–Laos railway's investment risk indicators more accurately. Combined with a modified rough set, the IM-DBSCAN algorithm is proposed to analyze the railway investment risk assessment. The contributions of this study can be summarized as follows: (1) Based on AP, DBSCAN, an integrated methodology AP-DBSCAN, which considers improving the parameter setting and efficiency, is proposed to classify railway risk indicators. (2) As AP-DBSCAN is a risk classification model rather than a risk calculation model, an IM-DBSCAN algorithm that consists of the AP-DBSCAN and a modified rough set is proposed to assess the railway investment risk. (3) Taking the China–Laos railway as a real-life case study, the effectiveness and superiority of the proposed IM-DBSCAN algorithm are verified through a set of experiments compared with other state-of-the-art algorithms.

The cities, for the most part, appeared up until the middle of the 1990s to be islands within the larger Chinese political economy in which job-secure workers could be certain that their livelihood, health, education, and living abodes would evermore undergird their and their children's sustenance. At least until the late 1980s, urbanites who stuck with the state sector even considered good treatment on the job a kind of birthright, an entitlement that was sure to be enforced. In the cities, true, there had always been the disadvantaged after 1949 – those without offspring or spouses, the disabled, and people unable to support themselves. But this relatively tiny batch of individuals generally survived in the shadows and out of sight, subsisting – but just barely – as members of the “three withouts” on a mere pittance, in the form of meager “social relief” disbursed by civil affairs departments.⁸