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Volume 3, Issue 3, 2025

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Virtual communities function as large-scale knowledge interaction systems in which users jointly produce, exchange, and validate knowledge resources. However, not all interactions contribute positively to system performance. This study examined how different forms of value co-destruction behavior degrade knowledge interaction processes and user-level value outcomes in virtual communities. Drawing on survey data from 530 users of firm-hosted virtual communities, a structural equation modeling approach was employed to analyze the effects of five negative interaction behaviors—irresponsible behavior, knowledge hiding, avoidance, conflict, and negative information interaction—on three dimensions of user value: practical, entertainment, and social value. The results indicate that avoidance, conflict, and negative information interaction significantly reduce practical value by impairing knowledge accessibility and information reliability. Knowledge hiding, avoidance, and conflict significantly reduce entertainment and social value by weakening interaction quality and relational embeddedness. Interestingly, irresponsible behavior increases individual entertainment and social value while simultaneously posing systemic risks to collective knowledge quality. These findings suggest that value co-destruction is not merely a behavioral problem but a systemic phenomenon that degrades knowledge flow efficiency, information quality, and collaborative stability in digital knowledge ecosystems. The study contributes to knowledge engineering research by identifying key failure mechanisms in knowledge interaction systems and offers governance implications for designing resilient and sustainable online knowledge platforms.

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The strategic siting of a military airport constitutes a high-stakes planning problem characterized by complex trade-offs, long-term operational consequences, and pronounced uncertainty in expert judgment. In contrast to civilian airport planning, where economic efficiency and environmental externalities are typically prioritized, military airport location decisions are governed by additional requirements related to operational security, survivability, logistical resilience, and future capacity expansion. To address these challenges, a hybrid Multi-Criteria Decision-Making (MCDM) framework is proposed for the systematic evaluation and selection of military airport locations under uncertainty. Six core criteria and their associated sub-criteria, reflecting operational, strategic, technical, and infrastructural considerations, were identified through expert consultation and domain analysis. Criteria weights were derived using the Defining Interrelationships Between Ranked Criteria II (DIBR II) method and its Fuzzy, Grey, and Rough extensions, enabling the explicit modelling of vagueness, incompleteness, and ambiguity inherent in subjective assessments. Expert evaluations were aggregated using the Einstein Weighted Arithmetic Average (EWAA) operator, which accommodates heterogeneous levels of expertise and mitigates dominance bias. Alternative locations were subsequently ranked using the Weighted Aggregated Sum Product Assessment (WASPAS) method, allowing for flexible integration of additive and multiplicative aggregation schemes. The robustness of the obtained rankings was examined through a sensitivity analysis of the WASPAS aggregation parameter $\lambda$, confirming that variations in the aggregation structure do not alter the identification of the optimal and least-preferred alternatives. Furthermore, a comparative analysis with five established MCDM techniques revealed a high degree of rank correlation, thereby reinforcing the internal consistency and reliability of the proposed framework. The results demonstrate that the integration of uncertainty theories with advanced MCDM techniques provides a rigorous and adaptable decision-support tool for military infrastructure planning. Owing to its modular structure and methodological generality, the proposed framework can be readily adapted to diverse geographical settings, operational doctrines, and security environments, offering practical value for strategic decision-making in the defense sector.

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Entity–relation extraction constitutes a fundamental step in the construction of domain-specific knowledge graphs. In fault analysis of transmission systems, this task is complicated by extensive entity–relation overlap, nested structures, and strong semantic dependencies in technical texts. To address these challenges, an entity–relation joint extraction framework integrating reinforcement learning with a global pointer network (GPN) is developed (joint extraction model based on GPN and reinforcement learning, RL-BGPNet). A fault-oriented dataset is first established from helicopter transmission system maintenance manuals and related technical documents. Global semantic associations are then captured through a relation-aware attention mechanism, while parallel decoding is achieved using a GPN to accommodate overlapping and nested entities. The extraction of entity–relation triplets is further formulated as a multi-step decision process under a reinforcement learning paradigm, enabling coordinated optimization of entity recognition and relation classification and alleviating error accumulation caused by task interference. Experimental evaluations demonstrate that the proposed framework maintains stable performance under complex semantic conditions and exhibits satisfactory generalization, supporting its application to knowledge extraction and preliminary knowledge graph construction in the helicopter transmission system fault domain.

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