Beijing Municipal Commission of Urban Planning

• A cross-modal spectral reconstruction framework transfers proximal spectra to satellite imagery for SOM mapping. • Reconstructed hyperspectral images outperformed Sentinel-2 and Zhuhai-1 in SOM estimation accuracy. • The spatial similarity-based Random Forest local strategy improved R 2 by 7.64 %. • Hyperspectral modeling benefits from soil samples processed to finer particle sizes. Remote sensing (RS) technology enables the rapid and accurate acquisition of soil organic matter (SOM) content, which is crucial for ensuring food security and promoting precision agriculture. Multispectral imagery is widely used for large-scale SOM mapping, but its limited spectral resolution substantially constrains estimation accuracy. While proximal hyperspectral data provide detailed spectral information, their point-based observations limit scalability across large regions. To overcome these limitations, a cross-modal modeling framework integrating proximal hyperspectral and satellite spectral data was proposed. Hyperspectral reconstruction technology was applied to enhance satellite spectral resolution and to extend proximal hyperspectral observations into spatially continuous imagery, achieving a balance between spectral accuracy and spatial continuity. To address SOM spatial heterogeneity, a spatial similarity-based random forest (SS-RF) local modeling strategy was introduced. Furthermore, the study systematically evaluated the impact of different soil particle size levels on spectral reflectance and SOM estimation accuracy. This study was conducted in a typical black soil region located in Northeast China. A multimodal dataset was constructed for SOM modeling, including in-situ and laboratory hyperspectral data with multiple particle size treatments, as well as satellite imagery from Zhuhai-1 and Sentinel-2A. The results indicated that the proposed cross-modal fusion and SS-RF framework demonstrated superior SOM estimation performance. The reconstructed hyperspectral imagery effectively integrated proximal and satellite spectral data, thereby preserving spectral integrity and enhancing their correlation with SOM. Among these, the reconstructed imagery based on finer particle sizes (100 mesh, ≤0.15 mm) exhibited the best performance (R 2 = 0.874, LCCC = 0.756, RMSE = 2.871 g·kg −1 , and RPIQ = 2.159), while the reconstruction using 50 mesh particles (≤0.35 mm) also achieved comparatively good accuracy (R 2 = 0.864). In contrast, the model constructed using field in-situ hyperspectral reconstructed imagery produced the lowest accuracy (R 2 = 0.730). The estimation accuracy based on the reconstructed imagery was significantly higher than that achieved using Sentinel-2A (R 2 = 0.712) and Zhuhai-1 (R 2 = 0.759). Compared to traditional global models, the proposed SS-RF local strategy improved accuracy, increasing R 2 by 7.64 %. This synergistic optimization approach, which combines spectral reconstruction, local modeling, and particle size standardization provides new insights and technical support for high-precision SOM estimation at the regional scale.

Abstract The dynamic coupling relationship between urban stormwater drainage networks and river water levels constitutes a critical issue in flood prevention. This study focuses on the area within Beijing's 3rd Ring Road, establishing an urban flood model using surveyed data of 67,212 pipe segments, 66,759 inspection wells, and 232 sub-catchments to analyze the backwater effect mechanism of river levels on drainage network capacity. Through Python-based batch simulations of 1,056 water level scenarios, results indicate that 64.6% of sub-catchments demonstrate drainage capacities meeting only 1-4-year recurrence interval standards. A nonlinear fitting formula (R²≥0.90) integrating pipe network service area, density, and storage capacity is developed, revealing a quadratic decay pattern in drainage capacity with rising water levels. These findings provide theoretical support for optimizing urban drainage design and flood warning systems.

Power load forecasting is a core component of power system scheduling and optimization, with direct impacts on grid security and economic performance. Among them, short-term load forecasting (STLF) plays a particularly critical role in electricity market transactions, grid operation optimization, and renewable energy integration. With the rapid growth of electricity demand and energy transition in the Beijing-Tianjin-Tangshan region, improving forecasting accuracy has become a research focus. This study addresses the STLF problem in the Beijing-TianjinTangshan region by constructing a systematic comparative analysis framework based on ERA5, the fifth-generation ECMWF global atmospheric reanalysis data. Multiple machine learning and deep learning models, including KNN, SVR, BP, CatBoost, LSTM, and CNN-LSTM, are comprehensively evaluated. The analysis focuses on prediction accuracy, computational complexity, and key influencing factors such as load features and model hyperparameters, thereby revealing the applicability of different methods. Results demonstrate that CatBoost and SVR achieve the highest prediction performance, with concentrated residuals and strong stability; LSTM shows reliable capability in time-series modeling; KNN exhibits the weakest generalization ability; while BP and CNN-LSTM are moderately effective in specific scenarios. The systematic approach proposed in this paper offers valuable insights into model selection, feature optimization, and forecasting mechanism analysis. Future work may focus on further optimizing model architectures, expanding forecasting regions and timescales, and incorporating additional external variables to enhance accuracy and stability.

Highway on-ramp merging areas represent critical bottlenecks that significantly impact traffic efficiency and sustainability. This paper proposes a novel Delay-Compensated Merging Control (DCMC) framework that addresses the practical challenges of cloud-based cooperative vehicle control under realistic communication conditions. The system integrates an efficient mixed-integer linear programming (MILP) model for trajectory optimization with a robust two-stage delay compensation mechanism. The MILP model coordinates mainline and ramp vehicles through proactive gap creation and speed harmonization, while the compensation framework addresses both deterministic and stochastic communication delays through Kalman filter-based prediction and real-time trajectory correction. Extensive simulations demonstrate that the DCMC system prevents traffic breakdown at near-capacity conditions (2200 vehicles per hour), achieving up to 31.6% delay reduction and 16.4% travel time improvement compared to conventional merging operations. The system maintains robust performance despite 2 s mean communication delays with 30 ms standard deviation, validating its readiness for practical deployment. By effectively balancing computational efficiency, safety requirements, and communication uncertainties, this research provides a viable pathway for implementing cloud-based cooperative control at highway merging bottlenecks to enhance both traffic flow efficiency and environmental sustainability.

Multi-voltage-level urban power network (UPN) is the basis for other infrastructure networks, especially water distribution network (WDN). Compared with other networks, UPN is more vulnerable to extreme events, including extreme weather events and deliberate attacks. The affection of UPN fault on WDN displays complex multi-stage characteristics. This paper first proposes a comprehensive tri-stage model of electrical-water fault propagation, including the electrical-water network connection model, the reduced-order hydraulic network model, and the water supply model of customers. Second, a scenario selection method and two resilience assessment indices are proposed. The indices measure the resistive capability of UPN considering the fault propagation effect. Finally, the proposed model and assessment method are verified by a simplified real-world urban power and water network based on the selected typical fault scenarios. The resilience assessment method helps electrical utilities to identify critical components of UPN and to make optimal decisions for long-term component hardening and short-term post-disaster restoration.

performance.Class I cap rocks present the best sealing capacity because they underwent intense mechanical compaction, abundant chemical compaction and calcite cementation, which contributed to the heterogeneous pore structures with poor pore connectivity.A four-stage conceptual model of pore evolution of cap rocks is presented to reveal how the diagenetic evolution of cap rocks determines the heterogeneity of their sealing capacity in carbonate reservoirs.

In July 2021, the NDRC (National Development and Reform Committee) launched the Notice to Further Better Off the ToU (Time of Use) Mechanism [1]. The notice is aimed to create a better environment for the development of the renewables and the construction of the renewables-based new power system by deepening the reform of the power market. This paper stands in the point of view of the power plants to address how does the power supply side, especially the coal fired power plants, tackle the challenges coming out in the context of the widening tariff difference between the peak and valley loads. This paper analyzed the operational tactics of the energy storage in the power supply side. With the model analysis and the simulation of the energy storage tactics, it tried to usher the power supply side to grasp the opportunity to adopt new energy storage facilities and apply the storage operational tactics, to enhance the reliability of the system, and meanwhile increase the proportion of the renewables into the power grid, ultimately help our power industry drive a successful energy transition and form a solid new power system in near future.