Henan Province is the birthplace of Chinese civilization and one of the earliest human settlements, which means that the area has an important national cultural heritage. Traditional villages are an important facet of this cultural heritage, and studying their spatiotemporal characteristics in different periods has important theoretical and practical significance for the sustainable development and protection of cultural heritage in the region. This paper takes the traditional villages of the Yellow River basin in Henan Province that were formed before 1919 as the research object. Information on the ancient river was obtained through a literature search as well as via field research, and the important tributaries of the Yellow River in different periods were mapped using ArcGIS 10.0 software. The nearest neighbor index, kernel density estimation, standard deviation ellipse and other methods were adopted to analyze the spatiotemporal characteristics of the traditional villages. The factors that influenced the evolution of traditional villages were explored in depth by combining changes in the course of the river and water conservancy projects. The results show that the formation of traditional villages along the Yellow River in Henan Province has experienced a historical track of growth, contraction, growth and prosperity and stability. The traditional villages along the Yellow River in Henan Province generally show a clustered pattern, forming a dense concentration of traditional villages in the middle reaches of the Yellow River. The center of gravity shows a migration trend from southeast to northwest. In terms of influencing factors, the spatiotemporal evolution of the relationship between villages and their distance to water is closely related to climatic fluctuations, changes in channel, water conservancy projects and social and cultural factors. This paper deepens our understanding of the relationship between traditional village evolution and watersheds by improving the consistency between village spatial distribution and historical geography and provides a useful theoretical reference for the sustainable development of China’s traditional villages.

Buffer zones help protect historical heritage sites’ integrity, authenticity, and sustainability, and thus are an essential measure for protecting such sites. Traditional buffer zone delineation considers mainly the urban texture, a rigid boundary, visual corridor construction, and other factors. A more direct approach is designating a rated outer periphery that includes historic properties as a buffer zone. However, boundaries determined by single-value orientation tend to omit protected objects. A buffer zone formed by boundary translation and enclosure will include many low-value areas in the protection scope, thereby wasting resources and affecting historic towns’ natural development, resulting in a severe contradiction between humanity and the earth. This paper proposes a new method for delimiting buffer zones. The method arises from the field that covers the value assessment of cultural heritage and from the principles of comprehensive protection, which combine cultural heritage’s tangible and intangible properties. We used 29 historical preservation plans as samples to obtain statistics on their protection content, selected natural and agriculture landscapes, architectural features, construction quality, number of stories in a building, building age, protection class, street pattern, historical elements, urban texture, cultural industry, and cultural elements according to the frequency of occurrence as the primary factors affecting a buffer zone’s boundary demarcation. These factors were weighed and evaluated by the multi-criteria decision-making method (MCDM) to create a comprehensive evaluation map for helping determine a buffer zone’s boundary. Using such method, we identified the value of historical heritage through employing the quantitative analysis method of MCDM. We also used the evaluation result to determine the buffer zone boundary objectively. This approach rectifies the problems found in previous studies, namely an incomplete accounting of all relevant factors and a lack of data; it also provides a definition of an historical heritage buffer zone.

The objective of this study was to clone the full-length cDNA of the APETALA1 (AP1) gene from lotus and analyze its sequence and expression pattern. The full-length cDNA sequence of the NnAP1 gene was amplified from the petals of Nelumbo nucifera 'Hongxia' using RT-PCR and rapid amplification of cDNA ends. Bioinformatic methods were used to analyze the sequence characteristics of the gene. Quantitative real-time PCR methods were used to investigate the expression pattern of NnAP1 in various organs and during different developmental stages. The cloned full-length NnAP1 cDNA (GenBank accession No. KF361315) was 902 bp, containing a 795-bp open reading frame encoding 264 amino acids with a relative molecular mass of 30,288.4 and an isoelectric point of 9.13. NnAP1 had a MADS-box domain and a K-box domain, which is typical of the SQUA/AP1 gene family. A protein sequence identity search showed that NnAP1 was 75-96% similar to other plant AP1s. Phylogenetic tree analysis indicated that NnAP1 was very closely related to AP1 of Glycine max, suggesting that they shared the same protein ancestor. Quantitative real-time PCR analysis showed that NnAP1 was expressed in various organs during different developmental stages; it had the highest expression in blooming flowers and had trace expression in the young vegetative and flower senescence stages. Our analysis suggests that NnAP1 plays an important role in controlling floral meristem identity and floral organ formation.

During urbanization in developing countries, fragmentation of green infrastructure due to increasing populations and the expansion of construction land leads to an extremely serious imbalance between the supply and demand for urban ecosystem services. In this study, the central city of Zhengzhou, a central city in central China, was selected as the study area and the excessive demand for six ecosystem services, namely, air purification, flood regulation, heat regulation, hydrological regulation, CO₂ sequestration and recreational services, was quantitatively evaluated. The entropy method was used to calculate the weights of various ecosystem services, and spatial overlay analysis was performed to obtain the comprehensive ecosystem service excessive demand. Finally, bivariate spatial autocorrelation analysis was used to explore the response of population density to comprehensive excessive demand for ESs. The results of this study indicate that: (1) The most prevalent need is for more CO₂ regulation service throughout the study area. (2) Except for hydrological regulation service, the spatial distribution of the remaining highly excessive ecosystem service demands are mostly concentrated in old neighborhoods. (3) Of the six excessively demanded economic services, rainwater regulation obtained the greatest weight, reflecting the poor urban infrastructure configuration for countering the rapidly increasing threat of flooding caused by climate change in the city. (4) The comprehensive ecosystem service excessive demand results show that there are eight priority green infrastructure implementation blocks in the central city of Zhengzhou. (5) There were three agglomeration types between population density and comprehensive excessive demand for ESs: high-high type, low-high type and low-low type. The spatial distribution characteristics of population density and comprehensive ES demand are positively correlated. The results of this study could help to provide information for decision making when delineating the priority areas and types of green infrastructure implementation in developing cities.

Plant diversity is the basis for human survival and development, directly affecting the function and stability of urban ecosystems. Its distribution pattern and causes have been a central issue in ecological and landscape gardening research. Rapid urbanization in Zhengzhou City has led to the fragmentation of urban green spaces and damage to ecosystems, seriously affecting urban biodiversity conservation. Understanding the distribution pattern of plant diversity in the region and its relationship with environmental factors is crucial for maintaining and enhancing urban plant diversity. Plant data from 178 sample plots in the built-up area of Zhengzhou City were collected and combined with environmental factors, and the characteristics of plant diversity, richness patterns, and their main environmental explanations in Zhengzhou City were explored. Results showed that there were 596 plant species belonging to 357 genera and 110 families in the study area. There were five dominant families and four dominant genera. Four distinct spatial patterns of plant diversity were identified along the urban–rural gradient. Urbanization factors such as GDP per capita, house prices, and imperviousness within 500 m from the patch significantly influenced plant diversity. There was an imbalance between the spatial pattern of plant diversity and application of urban landscape greening in Zhengzhou City. Future studies should focus on the application of native plants, curb plant homogenization, and reduce anthropogenic interference, which are conducive to protecting and enhancing urban plant diversity. These results can provide a basis for understanding the distribution pattern and influence mechanism of urbanization factors on plant diversity and serve as a reference for policymakers and planners of plant diversity conservation in Zhengzhou City.

As China’s urbanization shifts toward quality-oriented development, urban regeneration increasingly prioritizes the perceived quality of public spaces to enhance urban vitality and advance sustainable urban living. This study takes Zhengzhou’s Dongfeng Canal, a revitalized urban core waterfront, as a case to develop a human–machine collaborative analytical framework for exploring nonlinear relationships between visual environmental features and human spatial quality perception. By integrating 779 geolocated panoramic images with volunteers’ subjective rating data, this study adopts deep learning-based semantic segmentation to quantify eight objective visual indicators (e.g., greenness, color diversity, spatial structure). A random forest (RF) model links these indicators to three perceptual dimensions: scenic beauty, safety, and recreational value. Adopting explainable artificial intelligence (SHAP and PDPs), the results indicate that: (1) greenness is positively associated with positive perceptions but exhibits a significant threshold effect; (2) color diversity and waterfront accessibility substantially improve user experience, while excessive uniformity and extreme openness negatively affect perceived spatial quality. These findings challenge the simplistic linear “more-is-better” assumption in urban design and highlight the value of balanced, context-sensitive spatial interventions. This study provides evidence-based, segment-specific strategies for urban waterfront regeneration, advancing people-centered planning that integrates ecological functionality, social inclusivity, and long-term sustainability via Geospatial Artificial Intelligence (GeoAI) and geospatial analytics.