Military-civilian scientific and technological collaborative innovation (MCSTCI) is an important intersection of civil-military integrated development strategies and innovation-driven development strategies, and has become a brand-new model for Chinese key scientific and technological research. The selection of cooperative partner between military and civilian innovation entities is not only the primary segment to carry out MCSTCI activities, but also an important foundation for achieving complementary advantages and win-win cooperation. Therefore, on the basis of summarizing and combing the evaluation indicators, a partner selection model based on interval-valued intuitionistic fuzzy set multi-attribute decision-making is proposed. Firstly, we improve the traditional scoring function, and combine the new scoring function and entropy weight method to determine the weight of each evaluation indicator. Secondly, considering the impact of decision-makers’ risk attitudes, a method of transforming evaluation matrix based on hesitancy distribution is proposed. Finally, the grey correlation and TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method is used to mark and rank the candidates, and the innovation entities with the highest score is selected as the collaborative innovation partner. An example of a military equipment manufacturing enterprise in Beijing for partner selection in order to carry out joint technological research on a certain anti-ship missile weapon shows that the model is more comprehensive and flexible in solving fuzzy multi-attribute decision-making problems, and can assess and select cooperative partners for MCSTCI scientifically and objectively.

Aggregating distributed energy resources in power systems significantly increases uncertainties, in particular caused by the fluctuation of renewable energy generation. This issue has driven the necessity of widely exploiting advanced predictive control techniques under uncertainty to ensure long-term economics and decarbonization. In this paper, we propose a real-time uncertainty-aware energy dispatch framework, which is composed of two key elements: (i) A hybrid forecast-and-optimize sequential task, integrating deep learning-based forecasting and stochastic optimization, where these two stages are connected by the uncertainty estimation at multiple temporal resolutions; (ii) An efficient online data augmentation scheme, jointly involving model pre-training and online fine-tuning stages. In this way, the proposed framework is capable to rapidly adapt to the real-time data distribution, as well as to target on uncertainties caused by data drift, model discrepancy and environment perturbations in the control process, and finally to realize an optimal and robust dispatch solution. The proposed framework won the championship in CityLearn Challenge 2022, which provided an influential opportunity to investigate the potential of AI application in the energy domain. In addition, comprehensive experiments are conducted to interpret its effectiveness in the real-life scenario of smart building energy management.

In this study, we conceptualize the demands imposed on emergency supply chains during extraordinary emergency events as “stress” and develop a scenario-based stress evolution (SE) analytical approach in emergency mobilization decision-making. First, we characterize emergency supply chain stress by uncertainty, abruptness, urgency, massiveness of scale, and latency. Leveraging lifecycle theory and aligning it with the event’s natural lifecycle progression, we construct a dual-cycle model—the emergency event-stress dual-cycle curve model—to intuitively conceptualize the SE process. Second, taking China’s emergency medical supply chain as an illustrative example, we employ set theory to achieve a structured representation of emergency supply chain stress evolution (ESCSE). Third, we propose a novel ESCSE modeling methodology based on stochastic Petri nets and establish both an ESCSE model and a corresponding isomorphic Markov chain model. To address parameter uncertainties inherent in the modeling process, the fuzzy theory is integrated for parameter optimization, enabling realistic simulation of emergency supply chain stress evolution dynamics. Finally, the SE of the ibuprofen supply chain in Beijing during the COVID-19 pandemic is presented as a case study to demonstrate the working principle of the model. The results indicate that the ESCSE model effectively simulates the SE process, identifies critical states, and triggers actions. It also reveals the evolution trends of key scenario elements, thereby assisting decision-makers in deploying more targeted mobilization strategies in dynamic and changing environments.