Purpose: Various decision opinions comprise the foundation of emergency decision-making. However, decision-makers have difficulty establishing trust relationships within a short time because of decision-making groups being temporary. The paper aims to develop an ambiguity-incorporated opinion formation model that considers ambiguous opinions on relevant risks from a psychological perspective during the consensus reaching process.Design/methodology/approach: Addressing the problem of forming a consensus decision-making opinion in an ambiguous environment and relevant risk opinions, different social network structures were first proposed. Subsequently, psychological factors affecting the decision-makers' perception of ambiguous opinions and tolerance for ambiguity under the multi-risk factors were considered. Accordingly, an ambiguity-incorporated opinion formation model was proposed by considering the ambiguity and relevant opinions on multi-risk factors.Findings: A comparison between the ambiguity-incorporated opinion formation model and the F–J model illustrates the superiority of the proposed model. By applying the two types of network structures in the simulation process, the results indicate that the convergence of opinions will be affected by different decision-making network structures.Originality/value: The research provides a novel opinion formation model incorporating psychological factors and relevant opinions in the emergency decision-making process and provides decision support for practitioners to quantify the influence of ambiguous opinions. The research allows the practitioners to be aware of the influence of different social network structures on opinion formation and avoid inaccurate opinion formation due to unreasonable grouping in emergency decision-making.

China has shifted from a stage of rapid growth to a stage of high-quality development. This highlights the critical need for policy frameworks that synergistically align technological innovation with sustainable development. To address the research gap in systematically assessing the collaboration between New Energy Industry (NEI) policies and New Quality Productive Forces (NQPF), this study proposes a three-dimensional “Goal-Instrument-Value” framework. Methodologically, we employ a combination of deep learning models (including LDA topic modeling, LSTM networks, and the Soft EDA algorithm) and policy quantification methods, analyzing 135 NQPF policies and nearly 800 NEI policies. The findings reveal a significant and strengthening synergy between the two policy domains. Notably, a misalignment exists in the goal dimension, where the weight of science and technology in NEI policies remains modest at 20%, indicating substantial potential for enhancement. In the instrument dimension, there is a predominant reliance on economically driven instruments, along with a notable underutilization of environmental instruments. Nevertheless, the overall synergy in policy value, as measured by a specialized New-Force Dictionary and the BM25 model, exhibits a consistent upward trend. Based on these findings, we recommend strengthening investment in NEI technology R&D, increasing the deployment of environment-oriented policy instruments, and establishing a cross-departmental policy synergy mechanism. These measures are crucial to fully harness the synergistic potential of NEI and NQPF policies for accelerating China’s green industrial transformation and achieving its sustainable development goals.

The proliferation of digital services has led to a surge in cyber crimes, while traditional detection methods face bottlenecks such as labeled data scarcity, data imbalance, and poor adaptability to zero-day attacks. To address these issues, this study proposes CRAD-Defender, an end-to-end framework integrating self-supervised learning (SSL), graph neural networks (GNNs), and reinforcement learning (RL). It fuses MoCo v3 and GraphSAGE-GAT to capture individual behavioral features and cross-entity relational risks, adopts One-Class SVM for initial anomaly detection, and leverages DQN for closed-loop optimization of detection parameters. Experimental evaluations on UNSW-NB15 and CSE-CIC-IDS2019 datasets show CRAD-Defender outperforms baseline models with an F1-score of 0.91 and 0.89, ultra-low FPR of 1.1% and 1.3%, and low detection latency. Ablation studies verify the synergistic effectiveness of each core module, demonstrating the framework's superiority in accurate, real-time cyber crime risk classification and anomaly detection.

In the post-truth era, effective governance of emergency public sentiment faces significant challenges due to the phenomenon of opinion deviation. Although online public opinion has been extensively investigated, the specific impact of Public Opinion Deviation (POD) on the evolution of group negative emotion remains inadequately understood. To address this gap, this study proposes an explicable framework that integrates community detection, text mining, and opinion dynamics. Opinion deviation communities are identified by applying the Louvain algorithm and TextRank to social media data, followed by a deviation analysis of community topics against core issues. Subsequently, a multi-stage quantification model is constructed to measure the severity of POD. During these processes, we develop a novel Opinion Dynamics F-J model (POD F-J model) and its intervention-oriented model (IPOD F-J model), which incorporate the quantified POD severity to simulate the evolution of group negative emotion. Our findings demonstrate an intrinsic correlation between the severity of opinion deviation and the intensity of group negative emotion at the information level, thereby confirming the necessity of targeted intervention. Simulation experiments indicate that different intervention strategies should be adopted depending on the situation. Moreover, the application of a greedy algorithm identifies the time points corresponding to the peak severity of deviation and its onset as the efficiency-oriented intervention timings. This study provides a data-driven framework for monitoring and mitigating emotional contagion in deviation communities, contributing to both the theory and practice of digital governance.