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Using two nationally representative samples from 1988 and 1995, this study demonstrates that housing quality in urban China differs across time, housing types, and socioeconomic variables. But some key variables such as Communist Party membership, education, and the total family income are consistent predictors of housing quality across time and housing types. This study indicates that housing quality situations are very complex at the national level. The author concludes that more research into the quality of urban housing is needed, so that the outcomes of housing reform can be better assessed.

The purpose of this paper is to analyze the spectrum influence between radon transform and log‐polar transform when rotation and scale effect is eliminated. The average retrieval performance of wavelet and NSCT with different retrieval parameters is also studied.

The authors designed a multi‐scale and multi‐orientation texture transform spectrum, as well as rotation‐invariant feature vector and its measurement criteria. Then a new two‐level coarse‐to‐fine rotation and scale‐invariant texture retrieval algorithm based on no‐parameter statistic features was proposed. Experiments on VisTex texture database show that the algorithm proposed in this paper is appropriate for main orientation capturing and detail information description.

According to the experiments results, it was found that the combination of this two‐level progressive retrieval strategy and multi‐scale analysis method can effectively improve retrieval efficiency compared with traditional algorithms and ensure a high precision as well.

The paper presents a novel algorithm for rotation and scale‐invariant texture retrieval.

The purpose of this paper is to explore how GenAI can help companies achieve a higher level of hyper-segmentation and hyper-personalization in the tourism industry, as well as show the importance of this disruptive tool for tourism marketing.

This paper used the Web of Science and Google Scholar databases to provide updated studies and expert authors to explore GenAI in the tourism industry. Analysing hyper-segmentation and hyper-personalization modalities through GenAI and their new challenges for tourists, tourism cities and companies.

Findings reveal that GenAI technology exponentially improves consumers’ segmentation and personalization of products and services, allowing tourism cities and organizations to create tailored content in real-time. That is why the concept of hyper-segmentation is substantially focused on the customer (understood as a segment of one) and his or her preferences, needs, personal motivations and purchase antecedents, and it encourages companies to design tailored products and services with a high level of individual scalability and performance called hyper-personalization, never before seen in the tourism industry. Indeed, contextualizing the experience through GenAI is an important way to enhance personalization.

This paper also contributes to enhancing and bootstrapping the literature on GenAI in the tourism industry because it is a new field of study, and its functional operability is in an incubation stage. Moreover, this viewpoint can facilitate researchers and companies to successfully integrate GenAI into different tourism and travel activities without expecting utopian results. Recently, there have been no studies that tackle hyper-segmentation and hyper-personalization methodologies through GenAI in the tourism industry.