The Yangtze River Delta (YRD) region, located inside the Yangtze River Basin, functions as a vital ecological and economic area in China, with its natural environment directly impacting human existence. This study seeks to elucidate the spatial and temporal evolution of rural resilience in the Yangtze River Delta region and its underlying mechanisms by establishing a comprehensive assessment framework for rural resilience, thereby offering a scientific foundation and policy guidance for the region’s sustainable development. The research first established the DPSIR (driving force–pressure–state–impact–response) assessment index system. Subsequently, the entropy weighting method and TOPSIS were utilized to assess and rank the rural resilience levels in the Yangtze River Delta region (Shanghai, Jiangsu, Zhejiang, and Anhui) from 2012 to 2022. Ultimately, partial least squares structural equation modeling (PLS-SEM) was employed to examine the intrinsic logical relationships among the five dimensions of the DPSIR framework and to extract conclusions. The study effectively met the goals of SDG 7 (clean water and sanitation), SDG 8 (decent work and economic growth), and SDG 11 (sustainable cities and communities). The research indicated that (1) the resilience level in the Yangtze River Delta region initially declined, then increased, and eventually attained a condition of stabilization. Changes in the “driving force”, influenced by the “response level” and environmental “pressure”, have affected the resilience level of rural areas. There is heterogeneity in the assessment values and ranges of change among provinces, with the “impact” component exhibiting the most substantial evaluation value. The findings yield policy recommendations for the implementation of diverse regional governance, the establishment of connectivity mechanisms, the customization of strategies to address the specific deficiencies of each province, and the systematic enhancement of rural resilience.

Preserving ancient buildings can be improved using Building Information Modelling (BIM) models created from high-quality point-cloud data. The problems arise from the need for automatic extraction of the characteristics required to meet various security criteria from a high-fidelity point cloud. BIM for Traditional Wooden Architecture (TWA) constructions requires collaboration across various research fields. Two crucial concerns are needed to overcome the current gap and enhance the use of BIM: an automated model for the major components that smartly combines historical information and a Smart Modelling Framework (SMF) to represent these components. First, a parametric model for the usual components, highlighting similarities and properties, was created using a TWA structure as the basis. The next step is creating an automated modelling approach to determine the component type and hidden dimensions automatically. Conservation initiatives for traditional wooden structures will benefit greatly from this research results. The experimental results demonstrate that the suggested technique accomplishes better efficiency, reliability, and effectiveness than other existing technologies.

Edge detection is a crucial issue in computer vision, with convolutional neural networks (CNNs) being a key component in various systems for detecting edges within images, offering numerous practical implementations. This paper introduces a hybrid approach for edge detection in color images using an enhanced holistically led edge detection (HED) structure. The method consists of two primary phases: edge approximation based on parallel convolutional neural networks (PCNNs) and edge enhancement based on social spider optimization (SSO). The first phase uses two parallel CNN models to preliminarily approximate image edges. The first model uses edge-detected images from the Otsu-Canny operator, while the second model accepts RGB color images as input. The output of the proposed PCNN model is compared with pairwise combination of color layers in the input image. In the second phase, the SSO algorithm is used to optimize the edge detection result, modifying edges in the approximate image to minimize differences with the resulting color layer combinations. The experimental results demonstrate that our proposed method achieved a precision of 0.95. Furthermore, the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) values stand at 20.39 and 0.83, respectively. The high PSNR value of our method signifies superior output quality, showing reduced contrast and noise compared to the ground truth image. Similarly, the SSIM value indicates that the method’s edge structure surpasses that of the ground truth image, further affirming its superiority over other methods.

Polyester fibers tend to generate static electricity during the weaving and application processes, posing a threat to their production. Enhancing the water absorbency and electrical conductivity of polyester fibers themselves is an effective approach to improving their antistatic properties. In this study, multifunctional chitosan (CS), sodium phytate (SP), and Cu²⁺ were loaded on polyester fibers through layer-by-layer (LBL) self-assembly. The antistatic and water absorption capability of the modified polyester fibers was investigated by designing different process parameters combined with a surface resistance test and water contact angle tests. The antistatic property test results confirmed the positive effect of CS and Cu²⁺ on discharging electrostatic charge. Within a definite scope, with the increase in the number of assembly layers, assembly duration, and the concentration of the assembly substances, the wettability of the modified polyester fibers became more favorable and the antistatic effect became more remarkable.

Chinese lacquer, a natural polymer with exceptional durability and cultural significance, has been widely used since the Warring States period. This review examines recent advances in lacquer identification techniques and their role in cultural heritage conservation. Drawing on five representative case studies—the B54 Japanese armor, Ba lacquerware from Lijiaba, a Qing Dynasty folding fan, Ryukyu lacquerware, and late Joseon objects—we show how integrated analytical approaches combining microscopy, spectroscopy, chromatography, and biochemical methods provide critical insights into composition, degradation, and conservation strategies. Key findings highlight (1) the effectiveness of multi-technique analysis in characterizing complex lacquer–metal interfaces and layered structures; (2) the recognition of regional and chronological variations in lacquer formulations, highlighting the need for standardized authentication protocols and shared databases; and (3) the promise of non-destructive technologies to reduce sampling and improve aging simulations. By critically synthesizing these case studies, the review highlights both methodological successes and persistent challenges, such as ethical constraints of sampling and limited understanding of long-term degradation. Ultimately, lacquer is positioned at the intersection of material science and cultural preservation, offering a transferable framework for global heritage protection. Future directions include hyperspectral imaging, bioinspired consolidants, and computational modeling to advance non-invasive diagnostics and sustainable conservation.

This study explores the modification of traditional Chinese lacquer by incorporating boiled tung oil (BTO), boiled linseed oil (BLO), and turpentine oil (TO) to enhance its properties for gold leafing applications. Current traditional lacquers are limited by slow drying times and inconsistent surface quality, making their performance suboptimal for decorative gilding. The research addresses these gaps by investigating how varying oil types and concentrations (10%, 30%, and 50%) affect the lacquer’s drying time, viscosity, leveling properties, and overall gilding performance. Results indicate that TO-modified lacquer exhibits the best overall performance, showing the fastest drying time, highest glossiness, and smallest color variation, while BTO provides the smoothest surface and BLO ensures the best adhesion. These results demonstrate that the careful selection of oil type and concentration significantly improves lacquer’s functionality for gold leafing, offering a more efficient and aesthetically superior alternative to unmodified lacquers. This study provides valuable insights for optimizing traditional lacquer formulations for modern applications in gilding and decorative finishes.