In recent years, much more emphasis than before has been placed on fire safety regulations by the local and central authorities of China, which makes fire risk assessments more important. In this paper we propose a new fire risk assessment approach for large-scale commercial and high-rise buildings that aims to evaluate the performances of their fire safety systems; this should improve the fire risk management and public safety in those buildings. According to the features of large-scale commercial and high-rise buildings, a fire-risk indexing system was built, and based on it we established a scientific fire risk evaluation system. To this end, the fuzzy analytic hierarchy process (FAHP) was used to assign a reasonable weight to each fire risk factor in the evaluation system. In addition, we revised the original scores by analyzing the coupling relationships among the fire risk factors. To validate our system, we selected 11 buildings in Shandong province and collected their fire safety data. Then, we calculated the final scores for the fire safety management of those buildings, and the results show that: (1) our fire risk evaluation system can assign reasonable weights; (2) the proposed evaluation system is comprehensive and has strong interpretability, since it exploits the coupling relationships among the risk factors. The novelty of the proposed approach lies in that it integrates opinions from multiple experts and utilizes coupling relationships among the factors. Further, the feedback from the approach can find not only the weaknesses in fire risk management, but also the potential causes of fires. As a result, the feedback from our assessment can assist the safety chiefs and inspectors with improving fire risk management.

The increasing complexity of real-world engineering problems, ranging from manufacturing scheduling to resource optimization in smart grids, has driven demand for adaptive and high-performing heuristic methods. Automatic Heuristic Design (AHD) and neural-enhanced metaheuristics have shown promise in automating strategy development, but often suffer from limited flexibility and scalability due to static operator libraries or high retraining costs. Recently, Large Language Models (LLMs) have emerged as a powerful alternative for exploring and evolving heuristics through natural language and program synthesis. This paper proposes a novel LLM-based memetic framework that synergizes LLM-driven exploration with domain-specific local refinement and memory-aware reflection, enabling a dynamic balance between heuristic creativity and effectiveness. In the experiments, the developed framework outperforms other LLM-based state-of-the-art approaches across the designed AGV-drone scheduling scenario and two benchmark combinatorial problems. The findings suggest that LLMs can serve not only as general-purpose optimizers but also as interpretable heuristic generators that adapt efficiently to complex and heterogeneous domains.

Nowadays, deep peak-shaving of coal-fired boilers is critical for energy saving. However, the deep peak-shaving performance of coal-fired boilers is constrained by the performance of the catalyst of the SCR. This is because deep peak-shaving working condition will cause the temperature at the entrance of the SCR (Selective Catalytic Reduction) to drop below 300 degrees, which makes the catalyst of the SCR lose its normal function. The malfunction of the catalyst eventually causes the NOx emission concentration to exceed the standards, which limits the peak-shaving capability of the coal-fired boilers abiding by the ultra-clean emission standard. This paper proposes a new method to improve the peak-shaving capability of the coal-fired boilers using flue gas recycling technology (FGRT). With this new method, part of the flue gas is mixed with the primary air, which postpones the combustion of the coal powder in the coal-fired boilers and moves the combustion center of the coal powder upward. Using this method will increase the flue gas temperature at the exit of the coal-fired boiler, and therefore the temperature at the entrance of the SCR is increased to 303 degrees to achieve the high-efficiency zone of the catalyst. Moreover, after adopting the FGRT, the oxygen concentration in the coal-fired boilers is lowered, which can effectively reduce the concentration of NOx generated in the boilers. With these advantages, the FGRT can finally enable the NOx emission concentration of the coal-fired boilers reduce to 40 mg/Nm³, which meets the requirements of the ultra-clean emission at low-load conditions, and also improves the peak-shaving capability of the coal-fired boilers. Namely, the collaborative optimization of the deep peak-shaving and ultra-clean emission can be achieved together.