Liaoning University of International Business and Economics

This study examines the civil-military technology proximity-disclosure (PD) fit and its impact on the performance of civilian firms, with a comparative focus on China and the United States. In light of the growing global prominence of civil-military links, this research addresses the strategic tensions between technological proximity (access to military technologies) and technological disclosure (transparency to external stakeholders) in influencing firm outcomes. Leveraging data from 2,651 civilian firms across 82 countries, the analysis reveals distinct patterns between Chinese and American firms. Chinese firms, characterised by high proximity and lower disclosure, demonstrate superior performance when they maintain proximity to military technologies and increase their disclosure. Conversely, American firms perform better when they enhance proximity to military technologies while maintaining high disclosure levels. These findings suggest that national contexts significantly shape the optimal PD fit for firm performance, challenging universal approaches to civil-military collaboration in a single universal model. The study contributes to institutional theory by showing how different logics of conformance and legitimacy affect technological integration and firm outcomes in varied political and economic environments. One side of the logic and legitimacy is conformance to the military imperatives, and the other side is conformance to the market imperatives. Policymakers and firms can leverage these insights to craft context-specific strategies for navigating the complexities of civil-military alliances. • Through a comparative analysis of private enterprises in China and the United States, This study examines the civil-military technology proximity-disclosure (PD) fit and its impact on civilian firm performance. • Leveraging data from 2,651 civilian firms across 82 countries, the analysis reveals distinct patterns between Chinese and American firms. • The study advances institutional theory by showing how competing logics of conformance and legitimacy shape firm performance in civil-military alliances under differing national policies and cultures.

Background: Obstructive sleep apnea (OSA) affects approximately 1 billion adults globally, yet remains underdiagnosed, particularly in resource-limited settings. Physician knowledge and practice patterns are critical determinants of detection rates. This study assessed the knowledge, attitude, and practice (KAP) of physicians regarding OSA in two tertiary institutions in Nigeria. Methods: A cross-sectional survey was conducted among 73 physicians at the University of Port Harcourt Teaching Hospital and Rivers State University Teaching Hospital between April and September 2025. A Structured Questionnaire assessed OSA knowledge (symptoms, risk factors, diagnostics), attitudes toward screening, and clinical practice patterns. Descriptive statistics and chi-square tests were used for analysis. Results: Among 73 respondents (General Practice 31.5%, Internal Medicine 24.7%, other specialties. 43.8%), 95.9% had heard of OSA, but only 63.0% correctly identified polysomnography as the gold standard diagnostic test. While 60.3% rated their awareness as Good or better, only 16.4% received formal OSA training, and merely 6.8% felt adequately trained to manage OSA patients. Despite this, 84.9% expressed interest in continuing medical education (CME). Only 28.8% routinely or occasionally screened for OSA, and 35.6% had ever made an OSA diagnosis. The predominant challenges were lack of patient awareness (31.5%), limited access to sleep laboratories (26.0%), and lack of physician training (24.7%). Notably, 82.2% believed OSA is underdiagnosed, and 87.7% considered screening Very or Extremely important. Conclusion: Significant knowledge-practice gaps. exist among physicians regarding OSA. While awareness is moderate, formal training is severely lacking, resulting in suboptimal screening and diagnostic practices. The high interest in CME suggests that structured educational interventions could substantially improve OSA detection and management in similar settings.

The digital archives of South Korea's intangible cultural heritage contain multimodal resources-images, texts, and audio-posing challenges in cross-modal retrieval due to semantic complexity and the trade-off between accuracy and efficiency.This paper proposes a novel deep hashing network to address these issues.The model employs modality-specific encoders to extract features and a unified hashing layer to generate compact binary codes.A joint loss function is introduced to preserve cross-modal similarity while enabling effective quantisation, enhancing both discrimination and retrieval performance.Comprehensive evaluations on public datasets show that our method achieves a mean average precision of 92.7%, outperforming state-of-the-art approaches by 5.2%, while maintaining real-time retrieval speed.The framework offers a scalable solution that significantly improves accessibility and management for digital cultural heritage platforms.

Artificial intelligence (AI) technology is gradually integrating into the entire process of green supply chain management (GSCM), providing a systematic solution for enterprises to improve productivity and performance. This paper focuses on Chinese manufacturing enterprises, aiming to explore the multi-factor synergistic mechanism influencing differences in GSCM levels from a temporal perspective under the drive of AI. Based on 2019–2023 panel data of enterprises, this paper innovatively integrates the random forest algorithm with dynamic qualitative comparative analysis (QCA) to reveal the configurational effects of technological, organizational, and environmental factors in enterprises’ GSCM practices. The findings demonstrate that no single factor is a necessary condition for enterprises to implement GSCM; configurational analysis identifies two driving models: “AI technology innovation-driven (Configuration 1 and Configuration 2)” and “strategic resource-driven (Configuration 3)”; Configuration 1 combines research and development (R&D) investment and green awareness among executives with the enabling role of government subsidies; Configuration 2 couples R&D Investment with strong funding capacity, again facilitated by the presence of government subsidies; Configuration 3 combines AI technology adoption and green awareness among executives, supported by the necessary funding capacity and government subsidies. Additionally, inter-group analysis reveals no significant temporal effect among configurations but shows phased evolutionary characteristics. This paper has thoroughly explored the complex paths for enhancing GSCM of manufactory enterprises under the influence of AI. It is recommended that the government refine and strengthen targeted subsidy policies to better support the adoption and integration of AI in advancing GSCM within the manufacturing sector. Concurrently, manufacturers must align technology, organizational structure, and external factors, specifically through core AI technology improvements, enhanced executive green awareness, and the mobilization of government and external funding. These advancements have led to high-level GSCM within enterprises, allowing them to achieve high-quality and sustainable development.

This study focuses on the spatial organization patterns and vulnerabilities of the high-speed rail network in the three northeastern provinces of China (Liaoning, Jilin, and Heilongjiang). The Northeast region, with its unique political and historical background and relatively independent geographical location, has a relatively complete and developed railway network, especially characterized by a high density of conventional rail lines. In recent years, with the rapid development of high-speed rail, an increasing number of cities in the three northeastern provinces have been integrated into the high-speed rail network, leading to significant changes in the intercity connectivity network.

This study investigates the paths and effectiveness of integrating Curriculum Ideological and Political Education elements into college English teaching. By analyzing the inherent convergence between ideological-political elements and English teaching in terms of objectives and content, the research constructs a systematic integration framework centered on "principle guidance, content integration, method innovation, and process design." It proposes multiple integration strategies and designs a closed-loop teaching process. Simultaneously, an evaluation system based on multidimensional indicators and a mixed-methods approach is established to scientifically assess teaching effectiveness through quantitative and qualitative measures. The study demonstrates that this pathway effectively promotes the organic integration of ideological-political elements with language teaching, providing theoretical reference and practical paradigms for achieving whole-person education goals in college English teaching.

In order to further study the influencing factors of China's new energy stock price, this paper uses the modeling method to analyze the relationship between domestic and foreign energy prices and China's new energy stock price. Firstly, the relevance theory-spillover effect theory is introduced. Then, the construction process of vector autoregression (VAR) and generalized auto-regressive conditional heteroscedasticity (GARCH-BEKK) models are explained. Finally, the collected data are empirically analyzed by VAR and GARCH-BEKK model. Meanwhile, this paper uses Long Short-Term Memory (LSTM) model to forecast the share prices of new energy stocks and natural gas stocks. The results show that China's new energy stock price is influenced by international energy prices (natural gas prices and crude oil futures prices, etc.), and there is a two-way asymmetric spillover effect between international energy prices and China's new energy stock prices.

Abstract The advancement of globalization and the deep penetration of digital technologies have profoundly transformed organizational power structures and multicultural integration, thereby imposing higher demands on the dynamic balance of power distance between leaders and followers. Based on the social comparison theory, this study examines how the power distance congruence between leaders and followers affects employees' job role performance in the digital workplace. Using two-stage data collection, this study analyzed paired questionnaires data from 70 leaders and 265 followers in enterprises with a digital workplace, using polynomial regression, response surface analysis, and block variable methods for empirical testing. The results indicate that the higher of leader–follower power distance congruence, the stronger employees' positive followership behavior becomes. Compared with the scenario of high leader–high follower power distance congruence, low leader-low follower power distance congruence stimulates stronger positive followership behavior. Under the condition of incongruence, low leader-high follower power distance is more conducive to promoting positive followership than the reverse. Employees' positive followership behavior mediates the relationship between leader–follower power distance congruence and employees' job role performance. Specifically, a higher chronic promotion regulatory focus strengthens the mediating effect of positive followership behavior. This study enriches research on leader–follower power distance by offering a more comprehensive and dialectical explanation from a matching perspective, providing practical insights for enhancing employees' job role performance in the digital workplace.