Various physical and environmental factors affect the recreational experience in national forest parks. Understanding visitors’ preferences regarding these factors is significant for promoting the physical and mental health and well-being of urban residents. This study aims to quantify residents’ usage patterns and safety perceptions of national forest parks. It focuses particularly on Santai Mountain National Forest Park in Jiangsu, China, and conducted a semi-structured questionnaire survey with 688 participants. The statistical software IBM SPSS Statistics 24 was used for data analysis. A descriptive quantitative analysis was conducted on the respondents’ demographic information. Descriptive statistics, chi-square tests, and an ordinal logistic regression model were employed to analyze the survey data. The survey results show that most visitors go to the forest park only once or twice a year, usually on weekends, holidays, and during leisure time rather than on workdays. Because the national forest park is located in the suburbs, the journey is long, and most visitors choose to drive there. Additionally, the majority of visitors go to the forest park with family and friends. Because the park is well managed and has complete safety facilities, most visitors feel safe in the national forest park.

Recently, many Chinese cities have initiated improvement projects aimed at enhancing living conditions in older residential neighborhoods. Urban improvement should be closely linked to the needs of occupants to determine “what to improve”. Governmental initiatives and the various stakeholders involved in the project influence the impact of improvement efforts. The objectives of the study are essential to identify the factors influencing occupants’ satisfaction and to evaluate whether the occupants are satisfied with the improved old residential neighborhoods. This study conducts a post-occupancy evaluation (POE) of improved outdoor spaces in old residential neighborhoods, focusing on neighborhoods in Wuxi, China. A principal component analysis (PCA) was used to evaluate residents’ efficacy and satisfaction with the enhancements implemented in outdoor spaces. The methodology involved collecting data through surveys and on-site observations, which were then analyzed to identify the pivotal factors impacting the effectiveness of these improvements. The results indicated that enhancing outdoor spaces had a substantial positive impact on residents’ quality of life, social interactions, and physical activity levels. Additionally, the PCA identified accessibility, safety, and aesthetic quality as the main factors contributing to resident satisfaction. This study offers valuable insights for urban planners and policymakers aiming to rejuvenate aging residential districts, emphasizing the importance of data-driven approaches to improve the design and functionality of outdoor spaces.

The dung beetle optimization (DBO) algorithm is acknowledged for its robust optimization capabilities and rapid convergence as an efficient swarm intelligence optimization technique. Nevertheless, DBO, similar to other swarm intelligence algorithms, often gets trapped in local optima during the later stages of optimization. To mitigate this challenge, we propose the Move-to-Escape dung beetle optimization (MEDBO) algorithm in this paper. MEDBO utilizes a good point set strategy for initializing the swarm’s initial population, ensuring a more uniform distribution and diminishing the risk of local optima entrapment. Moreover, it incorporates convergence factors and dynamically balances the number of offspring and foraging individuals, prioritizing global exploration initially and local exploration subsequently. This dynamic adjustment not only enhances the search speed but also prevents local optima stagnation. The algorithm’s performance was assessed using the CEC2017 benchmark suite, which confirmed MEDBO’s significant improvements. Additionally, we applied MEDBO to three engineering problems: pressure vessel design, three-bar truss design, and spring design. MEDBO exhibited an excellent performance in these applications, demonstrating its practicality and efficacy in real-world problem-solving contexts.

This paper introduces the MMTADAN, an innovative algorithm designed to enhance cross-domain image classification. By integrating multi-scale feature extraction with Taylor series-based detail enhancement and adversarial domain adaptation, the MMTADAN effectively aligns features between the source and target domains. The proposed approach addresses the critical challenge of generalizing classification models across diverse datasets, demonstrating significant improvements in performance. The findings suggest that retaining essential image details through multi-scale extraction and Taylor series enhancement can lead to better classification outcomes, making the MMTADAN a valuable contribution to the field of image classification.

In the era of large-scale data, the role of image compression in computer vision(CV) and computer graphics(CG) tasks is increasingly critical. Traditional methods of image compression have reached their potential limits, leading to increased interest in deep learning-based techniques. However, these modern methods often compromise image quality and require extensive decoding times. This paper introduces the EICNet, which features the innovative Quick Depth-Residual Attention Module (Q-DRAM), an optimized post-processing module, and a checkerboard context model. This design aims to overcome typical shortcomings of deep learning-based compression, enhancing both training and compression efficiency as well as the quality of images at equivalent bit rates. The findings suggest that EICNet improves both the quality and efficiency of image compression. This approach marks a significant advancement in image compression technology, potentially benefiting future applications in the field. The code for this research can be accessed at: https://github.com/ziyicheng427/EICNet.