Changzhou City Planning and Design Institute

The transition to low-carbon urban environments is a cornerstone of global sustainability efforts. However, evaluating the diverse management efforts driving this transition is frequently hindered by fragmented and heterogeneous data. This study introduces a novel Retrieval-Augmented Generation (RAG) framework to systematically evaluate urban low-carbon governance. RAG represents a significant methodological innovation, integrating advanced information retrieval with generative artificial intelligence to provide a transparent and evidence-based assessment of policy implementation. The proposed framework is applied to evaluate the low-carbon energy practices of 296 Chinese cities. A critical finding is the systemic neglect of the “Check” phase across all urban tiers, posing a significant challenge to the long-term sustainability of carbon reduction initiatives. Conversely, high-performing cities are characterized by robust “Feedback” mechanisms. Policy priorities also vary significantly with city scale: larger cities emphasize strategic development and low-carbon transitions, while smaller cities focus on foundational planning and ecological preservation. This framework serves as a transparent, process-oriented tool for evidence-based low-carbon governance, thereby facilitating more resilient and sustainable urban pathways.

Understanding cultivated land use transition (CLUT) requires analytical frameworks capable of capturing the interconnected changes in production inputs, land use structure, and multifunctional outcomes. However, existing CLUT studies often rely on fragmented metrics that separately examine dominant or recessive transitions, limiting their ability to reveal the internal mechanisms of land use transition. Therefore, this study developed an integrated “factor-structure-function” analytical framework based on the theory of induced technological innovation. An evaluation system was constructed to operationalize the proposed framework, and Zhejiang Province—a rapidly urbanizing region in southeastern China, was selected as an empirical validation case to demonstrate its analytical value. The results showed that the integrated framework not only identified temporal and spatial patterns of CLUT, but also revealed internal trade-offs and synergies among factor substitution, structural reconfiguration, and functional transition that were not detectable using conventional CLUT metrics. In particular, the framework highlighted unique regional transition pathways driven by different modes of factor substitution. By connecting factor inputs, output structures, and land functions within the integrated framework, this study offers a practical tool for diagnosing CLUT and serves as a methodological guide for future CLUT research in rapidly urbanizing regions.

Lanthanide metal oxides have great application potential in electrochemiluminescence sensors. In this work, Tb3+ doped Eu2O3 (Eu2O3: Tb3+) was used as the luminescent group. Noble metal gold nanoparticles (Au NPs) are loaded on the surface of Eu2O3: Tb3+ to form Eu2O3:Tb3+@Au nanocomposites. Au NPs promoted electron transfer and generated SO4•- and OH• radicals to enhance the ECL intensity of Eu2O3:Tb3+, which played a role in preliminary signal amplification. In addition, Au NPs can also improve the biocompatibility of electrode materials and facilitate the immobilization of ACE aptamer (apt) on the electrode surface. The distance between deposited silver submicro particles (Ag SMPs) and Eu2O3:Tb3+@Au was controlled by the chain length of aptamer and cDNA to ensure the occurrence of a plasmon resonance effect. The wool-like Ag SMPs further increased the ECL intensity of the sensor by about 2.5 times. The dual signal amplification electrochemiluminescence aptamer sensor prepared can be successfully used for the detection of acetamiprid (ACE). The detection range is 1 × 10-15 M ~ 1 × 10- 8 M, and the detection limit of the sensor is 4.46 × 10-16 M (3σ/slope). The sensor was successfully applied to the analysis of actual samples.

Accurate monitoring of total suspended matter (TSM) concentration is essential for aquatic ecosystem protection and water quality assessment. Multispectral remote sensing provides an effective approach for large-scale TSM monitoring. However, robust retrieval models are difficult to develop due to limited in situ data. This study presents a Deep Feature Extraction–Machine Learning fusion framework that integrates a pre-trained back-propagation neural network (BPNN) with support vector regression (SVR) to enhance TSM retrieval. High-level spectral features extracted by BPNN are used as inputs to SVR (termed DFE-SVR) for regional TSM retrieval, using in situ measurements from five inland lakes in Jiangsu and Anhui Provinces, China. The generated TSM maps showed spatial patterns consistent with TSM concentration distributions visually observed in true-color imagery. Validation results demonstrated that DFE-SVR outperformed BPNN and SVR models, achieving R2 of 0.85 and 0.90 and RMSE of 7.95 and 4.76 mg/L for GF-1 and Sentinel-2 imagery, respectively. Compared with SVR models using principal component analysis or band combinations, DFE-SVR reduced RMSE by over 20%. Under reduced training samples, the DFE-SVR model also maintained higher stability and accuracy. These findings showed its potential for multispectral water quality monitoring with limited in situ data.

Modal analysis, which provides modal parameters including frequencies, damping ratios, and mode shapes, is essential for assessing structural safety in structural health monitoring. Automated operational modal analysis (AOMA) offers a promising alternative to traditional methods that depend heavily on human intervention and engineering judgment. However, estimating structural dynamic properties and managing spurious modes remain challenging due to uncertainties in practical application conditions. To address this issue, we propose an automated modal identification approach comprising three key aspects: (1) identification of modal parameters using covariance-driven stochastic subspace identification; (2) automated interpretation of the stabilization diagram; (3) an improved self-adaptive algorithm for grouping physical modes based on ordering points to identify the clustering structure (OPTICS) combined with k-nearest neighbors (KNN). The proposed approach can play a crucial role in enabling real-time structural health monitoring without human intervention. A simulated 10-story shear frame was used to verify the methodology. Identification results from a cable-stayed bridge demonstrate the practicality of the proposed method for conducting AOMA in engineering practice. The proposed approach can automatically identify modal parameters with high accuracy, making it suitable for a real-time structural health monitoring framework.

Seepage-induced internal erosion occurs when the hydraulic forces are sufficient to detach fine particles and transport them out of the structure, leading to notable changes in soil characteristics such as particle size distribution, pore size distribution, and pore structure, which will, in turn, have significant influences on the mechanical properties of soil. In this study, three approaches were utilized to model the erosion-induced loss of fine particles, i.e., deleting fine particles randomly (RM), by contact force (CF), and by coordination number (CN) using the discrete element method (DEM). The impact of each fine particle removal mode on both micro- and macro-mechanical soil properties, including peak strength, dilation, critical state characteristics, average particle coordination number, and contact force distribution, is comprehensively analyzed and compared. The results demonstrate that residual strength was insensitive to removal method, whereas at 10% fines loss, peak strength decreased by up to 17% and the secant stiffness E50 decreased by nearly 48%. This work provides a foundation for simulating the internal erosion of gap-graded soils.

ABSTRACTIn the context of the nearby enrollment policy in China, parents’ pursuit of good educational resources has driven up the prices of school district housing. Concerning this situation, the Education Department of Zhejiang Province released the enrollment warning information for primary schools in October 2018. This information disclosure aims to guide parents to purchase housing rationally by publishing the lists of schools exceeding their enrollment capacity. Despite the policy’s practical meaning, its impact remains underexplored. Using Hangzhou, China as the study area, the current study quantitatively analyzes the effectiveness of enrollment warning disclosure through the housing market. With housing transaction data from 2018 to 2019, the study constructs the hedonic price model, difference‐in‐differences model, and quantile regression model to identify both average and heterogeneous effects. The results indicate that school quality is significantly capitalized into housing prices and the disclosure of enrollment warning information intensifies price differentiation between warning and non‐warning primary school districts. In particular, the high‐priced housing market is more sensitive to information disclosure with stronger responses observed among upper quantiles. Rather than mitigating demand, the release of the information reinforces homebuyers’ preference for high‐quality schools. The empirical results and implications of this study are helpful to the continuation and optimization of warning information policy, contributing to education equity and sustainable urban development.

Metro system construction has profoundly influenced the Chinese housing market. As metro networks in major cities evolve from single lines to complex multi-line systems, the associated benefits extend beyond station accessibility to enhanced network connectivity. However, the impact of improved metro network connectivity on housing prices has been largely overlooked, leading to a potential underestimation of its economic benefits. Using housing transaction data from 2018 to 2023 during the metro network expansion in Hangzhou, this study employs a hedonic pricing model and the difference-in-differences method to analyze the effects of network expansion on housing prices around existing stations. The results show that metro network expansion significantly enhances connectivity and increases housing prices within 0.5 km of existing stations by an average of 2.42%. The findings also reveal heterogeneity in these effects. Geographically, the premium effect in non-central urban areas (1.67%) slightly exceeds that in central urban areas (1.56%), and areas with higher station density benefit more from network expansion. The degree of network connectivity improvement further shapes these effects, with the highest price premium (2.49%) observed near stations with moderate connectivity improvements, outperforming those with high or minimal connectivity improvements. By highlighting the capitalization effects of metro network expansion, this study provides new insights into how public transportation infrastructure influences housing markets in China.