Assessment of water resource spatial equilibrium (WRSE) is crucial for regional sustainable development, yet traditional methods always face difficulties in quantifying systemic differences and resolving their internal uncertainties. Accordingly, this study proposes a novel multi-attribute decision-making (MADM) model that integrates symmetric interaction entropy (SIE) with connection numbers (CNs) within a variable-weight framework. Firstly, information differences between alternatives and an ideal state were quantified by SIE, then these differences were decomposed into certain and uncertain components through the “identity–difference–opposition” (IDO) idea of CNs. In addition, a variable-weight mechanism was incorporated to enhance the model’s adaptability to regional characteristics. Applied to evaluate the WRSE in the Beijing–Tianjin–Hebei (BTH) region from 2014 to 2023, the model reveals that Hebei maintains the most favorable equilibrium state, with a partial identity potential or equal potential, followed by Beijing, while Tianjin predominantly exhibits partial opposite potential due to pronounced conflicts between its resource endowment and industrial structure. The proposed model not only enhances the sensitivity and interpretability of evaluation results but also facilitates the identification of key vulnerable indicators, thereby providing a scientific basis for formulating differentiated regional water governance strategies.

Interdisciplinary teaching is a pivotal strategy for deepening disciplinary theory and broadening students’ cognitive boundaries, crucial for the sustainability of education. By considering scientific knowledge’s humanistic background and technological evolution, this study proposes a novel interdisciplinary teaching framework based on the Source–Knowledge–Use (SKU) paradigm. Then, taking fuzzy mathematics as a case, the Humanities–Science–Technology Model (HSTM), based on a tripartite progression from humanistic foundations to scientific principles and then to technological applications, was established. This study systematically expounds the HSTM’s framework, contents, and implementation design, while critically examining potential challenges and corresponding mitigation strategies. The proposed SKU-based interdisciplinary teaching framework not only provides methodological guidance for interdisciplinary instruction in fuzzy mathematics but also offers transferable insights for cognate disciplines seeking to implement sustainable educational practices.

Integrated Energy Systems (IESs) are characterized by strong nonstationarity, seasonal heterogeneity, and stochastic fluctuations in electric, cooling, and heating loads, making accurate and reliable probabilistic forecasting challenging. To address this issue, this study proposes an Improved Osprey Optimization Algorithm-based TCN-BiLSTM-Quantile Regression (IOOA-TCN-BiLSTM-QR) model for short-term probabilistic forecasting of multi-energy loads. The model integrates Logistic chaotic mapping and Lévy flight to enhance the optimization capability of the Osprey Optimization Algorithm, while TCN and BiLSTM are employed to capture multiscale local features and long-term temporal dependencies, respectively. Quantile regression is further introduced to construct 90% prediction intervals and quantify forecasting uncertainty. Using year-round hourly operational data from a university IES, the proposed model achieves average PICP and PINAW values of 90.50% and 0.1513, respectively, indicating a favorable balance between interval coverage and width. For point forecasting, the model achieves test-set R² values of 0.9385, 0.9781, and 0.9731 for electric, cooling, and heating loads, respectively, with corresponding MAPE values of 2.61%, 2.67%, and 2.36%. Comparative and ablation experiments demonstrate the effectiveness of the integrated optimization and temporal feature extraction framework, while cross-dataset validation further confirms its applicability to different multi-energy load conditions. Overall, the proposed model provides accurate point forecasts and 90% prediction intervals with average coverage close to the nominal level, offering quantitative information for reserve capacity allocation, operational scheduling, and risk-aware decision-making in IESs.

Urban floods increasingly threaten the mega-regions’ sustainable development, yet the pace and causes of change in urban flood resilience (UFR) remain elusive. This study proposes a new index system for UFR from three dimensions: resistance, recovery, and adaptability. The system includes 18 indicators across natural, economic, social, and infrastructure aspects. A comprehensive evaluation model combining entropy weighting, Criteria Importance Through Intercriteria Correlation (CRITIC), and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) methods is developed and validated for the Beijing–Tianjin–Hebei (BTH) region of China, covering 2011–2022. Spatial dependence is diagnosed with global and local Moran’s I statistics, while an Extreme Gradient Boosting-Shapley Additive Explanations (XGBoost-SHAP) isolates the contribution of each driver. The results indicate that UFR in the BTH region exhibited a generally increasing but fluctuating trend. Spatially, UFR displays a pronounced gradient, with higher levels concentrated in the northwest and lower levels in the southeast. Significant spatial autocorrelation is observed, spatial autocorrelation strength ranging from 0.330 to 0.404. Key drivers contributing to UFR include urban slope, hydrological station density, per capita park green space area, and population density, all with SHAP importance values exceeding 0.02 (ranging from 0.0012 to 0.1343). These indicators collectively play a dominant role in shaping the region’s resilience dynamics, highlighting their crucial influence on sustainable urban development.