Urban floods pose a serious threat to human life, property safety, and socio-economic development. Urban flood modeling plays a vital role in disaster prevention and resilient infrastructure planning. Traditionally, one-dimensional (1D) drainage network and two-dimensional (2D) surface flow coupling models based on physical processes have been widely used to simulate urban flooding. However, such models require substantial computational resources and long execution times, making them unsuitable for real-time flood forecasting over large areas. This study proposes a CNN–Transformer model as an efficient surrogate for physical models in rapid urban flood forecasting. The model is trained using high-resolution gridded precipitation data and inundation simulation outputs generated by a calibrated 1D/2D hydrodynamic model, covering 5,475,655 grid cells within the study area. By integrating the spatial features of rainfall and the temporal dynamics of flood evolution, the model enables fast and reliable predictions. The study shows that the CNN-Transformer model closely matches the physical model in predicting flood depth, inundation extent, and flood volume, achieving a mean relative error below 10%, NSE ≥ 0.956, and R2 > 0.958. Additionally, the model reduces computation time by approximately 287.4–308.8 times compared to the physical model, meeting the real-time requirements of flood emergency response. These results indicate that the proposed method significantly improves computational efficiency while maintaining high predictive accuracy. It contributes to the development of smarter and more sustainable urban decision-making systems and holds strong practical value for real-time flood risk management, infrastructure planning, and climate resilience enhancement.
An LLM-as-judge score can move even when the candidate responses stay fixed, simply because the evaluator has changed. We treat this evaluator-replacement ambiguity as a measurement-validity problem. Across four judgment datasets, we compare two upgrade paths available in practice: scaling Qwen3 dense judges from 1.7B to 32B parameters and moving across MiniMax M2-M2.7 released APIs. The main pattern is that judge upgrades are not interchangeable: only Qwen3 1.7B to 4B gives a robust adjacent gain, while MiniMax adjacent releases do not. Stronger judges reduce but do not remove position and verbosity bias. Repeated-sample juries add little when errors are correlated. Structured debate can move decisions substantially, but without parser and fallback logs those shifts cannot be attributed to deliberation. We argue that LLM-as-judge reports should include dataset slices, bias probes, error-dependence estimates, and protocol audit trails.
The existence of the hydrate memory effect has always been a “double-edged sword”. On one hand, it can accelerate the formation rate of hydrates, which helps address the issue of low hydrate formation rates. On the other hand, its presence can lead to secondary blockages, posing a significant challenge to the oil and gas industry. Currently, there is insufficient understanding of the process of the fading of the hydrate memory effect. This study focuses on the fading process of the hydrate memory effect, utilizing molecular simulations to demonstrate the microscopic processes of memory effect fading at the molecular scale. The simulation results indicate that shorter dissociation times result in a longer persistence of the memory effect. Nanobubbles do not enhance the likelihood of the memory effect occurring; instead, ordered water molecules and supersaturated methane molecules are the key factors. The fading process of the memory effect includes the escape of methane from the liquid phase and the breaking of hydrogen bonds between ordered water molecules. Furthermore, we propose measures to promote the fading of the memory effect. These research findings will provide important theoretical foundations for the application and prevention of the memory effect.
We prove that every countable meet-continuous lattice is Scott sober. For any family of such lattices, the Scott topology on the product equals the product of the factor Scott topologies, and the product Scott space is sober. These results answer affirmatively Questions 7.1 and 7.2 of Xu (arXiv:2609.18032v1), extending the countable-frame results beyond finite distributivity. A diagonal argument on meet coordinates extracts directed punctured intervals from nonprincipal ideals. Join translates of these intervals form a countable family detecting Scott openness and yield Scott open rectangles by successive finite choices. We also show that the cardinal spectrum of Scott non-sober meet-continuous lattices is upward closed and, under the Continuum Hypothesis, consists of all uncountable cardinals.
