As the basic unit of a smart city, the smart community has received considerable attention and problems in community governance have appeared simultaneously. Previous studies of smart community governance have failed to encompass all aspects, especially the evaluation tools for ensuring its outcomes. Therefore, this paper developed a comprehensive evaluation framework based on the CRITIC-TOPSIS method combined with the identified evaluation indicators. Seven smart communities from four cities in China were selected as cases to show how this evaluation framework could be applied to decision-making. The results indicated that the evaluation indicator ”Mediation of Conflict” had the highest weight while ”The participation of social enterprises in governance” had the lowest weight. Furthermore, the Yucun community presented the highest governance performance among these seven smart communities. Several strategies are proposed for improving the level of smart community governance, such as devoting significant resources to develop infrastructure in smart communities, facilitating communication among multiple participants, and increasing funding for the implementation of smart communities. This research contributes both to the innovation of community governance evaluation and to the improvement of smart communities.

As a crucial paradigm for addressing urbanization-related problems, smart community construction is in full swing, and its goal is to enhance residents’ sense of gain. Prior studies have not been able to account for all aspects of smart community construction, especially the evaluation tools from the perspective of residents’ sense of gain. Therefore, this paper seeks to establish a comprehensive evaluation framework for residents’ sense of gain in the smart community through the integrated method, which includes the entropy weight method (EWM), the fuzzy comprehensive evaluation (FCE), and the obstacle degree model (ODM). For the purpose of verifying the feasibility of the evaluation framework, 31 smart communities in 6 Chinese cities (Shenzhen City, Putian City, Huizhou City, Dongguan City, Zhengzhou City, and Luoyang City) were selected. The results indicated that the weight of “Cultural activities for the elderly” indicator is the highest while the “Overall design” indicator is the lowest. In addition, Putian City had the best performance, but Shenzhen City ranked last among the six cities. Moreover, among the 31 communities, the Fengshan community in Putian City performed the best while the Xinglong community in Luoyang City performed the worst. Several suggestions are proposed to improve residents’ sense of gain in smart communities, such as enhancing the quality of healthcare services, meeting the needs of the elderly through multiple channels, and enriching business services. This study not only innovates the evaluation method of smart community construction from the perspective of residents’ sense of gain but also provides suggestions for promoting the sustainable development of the smart community and enabling residents to feel more satisfied.

The rise of “Internet + Recycling” platforms is transforming the domestic waste management landscape, creating dual-channel reverse supply chains where new platforms interact with traditional recyclers. However, these platforms face critical strategic decisions regarding their service portfolios (convenient but costly door-to-door vs. economical fixed-point drop-off) and their relationship with incumbents (cooperation vs. competition). This study aims to determine the optimal pricing, service level, and relationship strategies for an “Internet + Recycling” center to maximize profitability under the influence of consumer channel preferences and government subsidies. We developed four Stackelberg game-theoretic models representing different scenarios of service modes (fixed-point only vs. fixed-point with door-to-door) and relationship structures (cooperation vs. competition). We derived equilibrium solutions for recycling prices, service levels, and profits. Our results reveal that while cooperation generally leads to higher systemic profits, the addition of a door-to-door service significantly alters the strategic landscape. We find that a higher consumer preference for the platform channel allows the center to lower prices while increasing profits, and that government subsidies are the most effective at enhancing service levels in cooperative models. Crucially, intense competition incentivizes recycling centers to reduce rather than increase their service levels to cut costs. This research provides a decision-making framework for recycling enterprises to select optimal service and competitive strategies. It also offers insights for policymakers on how to design subsidies to effectively promote high-convenience recycling services and foster a more efficient circular economy.

With the rapid growth of global waterborne tourism, the passenger capacity of ships continues to increase, which means passenger evacuation safety is becoming a critical concern. Existing passenger ship evacuation prediction methods mainly rely on computationally intensive evacuation simulations, which suffer from high computational cost. This limitation hinders rapid evacuation assessment and emergency decision-making for large passenger ships. The objective of this study is to develop a rapid and interpretable surrogate prediction model for passenger ship evacuation time. To achieve this objective, Sobol sequence sampling is employed to efficiently construct representative evacuation scenarios, while Bayesian optimization is used to improve the prediction performance of the Random Forest model. The results show that the Sobol sequence sampling method efficiently designs multi-scenario evacuation cases for the passenger ship. The optimized model achieves high prediction accuracy with an R² of 0.901, effectively capturing the nonlinear relationship between evacuation factors and total evacuation time. Feature importance analysis demonstrates that stairways near the embarkation stations and passengers positioned farther away from them significantly affect total evacuation time, highlighting the spatial disparity in evacuation efficiency. This study provides a reliable data-driven approach and technical support for emergency decision-making and safety management in passenger ships.

University libraries are critical campus venues characterized by high-density occupancy, making comprehensive fire evacuation safety assessment essential for protecting lives and cultural assets. Conventional evaluation methods often suffer from either subjective bias or a lack of engineering realism, while standalone cloud models lack robust weight allocation mechanisms. To address these deficiencies, this study proposes a fire evacuation safety evaluation method integrating combined weighting and the cloud model. An evaluation framework consisting of 21 indicators is established across four dimensions: human factors, evacuation facilities, emergency management, and venue environment. For weight determination, the Analytic Hierarchy Process (AHP) and the entropy method are synthesized to balance expert experience with data-driven objectivity. The cloud model is then employed to handle the inherent fuzziness and randomness in safety assessment, utilizing the forward and backward cloud generators to map qualitative linguistic descriptions to quantitative values based on three digital characteristics: expectation (Ex), entropy (En), and hyper-entropy (He). A case study of a university library in Wuhan is conducted. The results demonstrate that the library’s overall fire evacuation safety level is "Excellent," with a comprehensive cloud parameter set of (83.577, 0.620, 0.116). The assessment reveals that while evacuation facilities perform optimally, the complex architectural layout represents a structural vulnerability, and personnel safety literacy requires further stabilization. This integrated framework effectively overcomes the limitations of single-weighting strategies and provides a reliable, fine-grained quantitative tool for fire safety management and targeted risk mitigation in high-density public buildings.