Accurate and efficient forecasting of energy consumption is a crucial prerequisite for effective energy planning and policymaking. The BP neural network has been widely used in forecasting, machine learning, and various other fields due to its nonlinear fitting ability. In order to improve the prediction accuracy of the BP neural network, this paper introduces the concept of forecast lead time and establishes a mathematical model accordingly. Prior to training the neural network, the input layer data are preprocessed based on the forecast lead time model. The training and forecasting results of the BP neural network when and when not considering forecast lead time are compared and verified. The findings demonstrate that the forecast lead time model can significantly improve the prediction speed and accuracy, proving to be highly applicable for short-term energy consumption forecasting.

In the new context of information sharing to reshape the supply chain’s interruption risk propagation mechanism, this paper focuses on the interruption risk propagation and resilience of the supply chain of emergency medical supplies in the background of emergencies. Firstly, an emergency supply chain’s risk propagation model under information sharing is proposed by combining the information sharing mechanism with the supply chain’s risk propagation theory. Secondly, an interruption risk propagation model for the supply chain of emergency medical supplies based on a Bayesian Network is constructed, and the do-calculus technique is introduced to transform the intervention effect of information sharing into the quantification of the supply chain’s risk probability. Thirdly, a system dynamics method is used to construct a supply chain model for emergency medical supplies, which takes into account different interruption scenarios caused by emergencies and evaluates the supply chain’s resilience through the key variable “demand fulfillment rate” in the model. The results of the study indicate that the impact of different types of interruption scenarios on supply chain resilience varies significantly. Information sharing can effectively reduce the negative impact of interruption risk. This study provides theoretical basis and practical guidance for improving the resilience of the supply chain of emergency medical supplies, which is of great significance for maintaining social stability and promoting the sustainable development of the public health system.

Dynamic decision-making is the process of seeking optimal choice with multiple related attributes under the multi-time-point situation. Considering that the time-varying nature of decision information can have a specific impact on the psychology of decision makers, in this paper, a dynamic decision-making method based on the cumulative prospect theory is proposed. Combining this with infinite parameterization of fuzzy soft sets, a time series interval triangular fuzzy soft set is presented, which has characteristics of boundedness, monotonicity, and symmetry. Then, based on the new information priority principle, the exponential decay model is used to determine the time weight coefficient, and a static fuzzy soft matrix is obtained. Furthermore, a method of calculating psychological utility values is proposed, in which the threshold-reference point set is introduced to analyze the psychological “profit and loss” values. Simultaneously, the time probability of the decision-making scenario is transformed into the corresponding weight function. On the basis of prospect maximization theory and maximum entropy theory, an optimization model for determining the weight of decision parameters is established. The cumulative prospect values of the alternatives are given to achieve the best choice for the alternatives. Finally, an example showed the feasibility and effectiveness of this method.

In China, communities function as grassroots self-governing bodies, and the enhancement of public participation in community governance has remained a central focus of study. This paper applies the Large-Scale Group Decision-Making (LSGDM) method to the process of community self-governance and proposes a dual feedback group consensus decision-making model that takes into account the unique social relations among residents. Firstly, the concept of the Organizational Invisible Field—formed in communities by intangible social capital such as positional power and interpersonal relationships within the organization—is introduced. The definition of Invisible Field Force is utilized to measure the influence of these forms of capital on social relationships. Subsequently, drawing on field dynamic theory, the process by which residents’ preferences within the organization are shaped by the action of Invisible Field Force is explored. Secondly, acknowledging that invisible relationships can be affected by dynamic interactions during the decision-making process, the Invisible Field Force change model is constructed. Building on this, a dual feedback consensus coordination mechanism—encompassing both in-organization members and all residents—is designed. Finally, the validity and utility of the model are verified through case studies and sensitivity analyses.