Water bodies are regarded as effective tools to improve urban thermal environment. However, there is little information regarding the impacts of the water bodies under different climatic conditions. In this study, the thermal effects of similar water bodies were investigated in a temperate monsoon climate city (Xi’an) and a humid subtropical climate city (Chengdu) in summer. Results showed that both the water bodies reduced the air temperature and increase the humidity in their vicinity, and the closer to the water body the more obvious the effects. However, the water body in Xi’an could provide cooling to its surroundings all day, while the one in Chengdu had warming effect at night. In this study, the universal thermal climate index (UTCI) around the water bodies was also investigated. It was found that both water bodies would improve the thermal comfort in their vicinity, and the thermal comfort achieved by the cooling effect was not cancelled out by the humidity change. In addition, it was found that the actual thermal sensation of the human body can be different for same UTCI values in Xi’an and Chengdu, which indicated the necessity to modify the thermal benchmarks before investigating the thermal comfort in different cities.

As an indicator of grain safety, grain temperature data assumes great importance to the analysis of grain storage condition and decision-making of preventive measures such as ventilation and cooling. However, obtaining a thorough picture of grain temperature distribution via grain IoT with sensors deployed in the granary remains a challenge, given numerous data gaps across various areas due to an insufficient coverage of the sensor network that fails to encompass the entire granary. Interpolation of grain temperature data, in this regard, is able to fill in the ’unsensored’ areas that are vacant in the records of data. Yet, little literature is found in the frontier scholarship of grain temperature interpolation. To fill this noticeable niche, this study develops a novel data fusion interpolation model named CAMNN featuring an integration of Convolutional Neural Network (CNN), attention mechanism, and Multilayer Perceptron (MLP). CNN is used to capture local spatial features of the temperature data, the attention mechanism enables the location of key and sensitive temperature areas, and MLP is incorporated for deep feature fusion. Performances of the proposed model are evaluated in a bin granary located in Shaanxi, China, and further validated in a larger bin granary of different storage types situated in Ningxia, China. Comparative assessments are conducted with five machine learning and deep learning models. Results indicate that CAMNN outperforms the other models, with a MAE of 0.5251 and a MSE of 1.0881, demonstrating robust cross-context applicability across bin granaries varying in terms of sizes, storage types, and climatic zones.

As urban spaces and cultural contexts continue to evolve, the conservation of immovable cultural heritage faces increasing challenges. This study adopts museumification as an extension of the living heritage continuity approach and examines how a religious monument can be re-embedded in contemporary social life. Using the Jin dynasty Sansheng (Three Saints) Pagoda in Qinyang City, Henan Province, as a case study, it analyzes the mechanisms through which museumification reshapes heritage value and public engagement. A three-dimensional analytical framework—field, space, and society—was developed, and data were collected through field observation, 205 questionnaire responses, and in-depth interviews with museum staff. The results show that museumification has enhanced the pagoda’s public visibility and symbolic meaning, strengthened local identity, and supported the transmission of historical knowledge. Visitors reported cultural pride, historical immersion, and emotional connection, demonstrating the museum’s role in maintaining the living continuity of the site. However, challenges such as limited exhibition space, insufficient narrative coherence, and the tendency for emotional experience to outweigh knowledge acquisition remain. Overall, the study offers empirical insights into the contemporary transformation of religious heritage and provides practical implications for conservation strategies based on museumification.

Soil organic carbon (SOC) is pivotal to the terrestrial carbon cycle and climate regulation, yet its spatiotemporal dynamics and future climate responses across soil layers remain insufficiently understood in mountainous ecosystems. Taking the Qinling Mountains, a typical mountainous ecological barrier in central China with a total area of approximately 38.18 × 10⁴ km², as the study area, we analyzed historical SOC changes (1980s–2010s) and projected its future dynamics under different scenarios using a validated Random Forest model (R² = 0.81 for 0–20 cm, SOC₂₀; 0.73 for 0–100 cm, SOC₁₀₀), and further disentangled dominant drivers. Results showed historical mean SOC density increased, with higher storage in western/central high-elevation zones and lower values in southern/eastern low-elevation areas. Climate was the primary driver of SOC₂₀ dynamics, while SOC₁₀₀ was jointly regulated by climate, vegetation, and environmental factors, indicating weakened climatic control with increasing soil depth. Precipitation increases partially offset warming-induced SOC loss, leading to small changes in regional mean SOC density, but strong spatial heterogeneity resulted in substantial total SOC stock losses (SOC₂₀: −1.41 to −6.59 Tg C; SOC₁₀₀: −6.86 to −28.76 Tg C), with net losses in high-elevation zones and gains in low-elevation areas. SOC within the whole 1 m soil profile exhibited larger climate-driven changes than topsoil. These findings advance understanding of SOC dynamics in complex mountainous ecosystems and provide key scientific insights for regional carbon cycle assessments under climate change.