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The mobility of economic factors across jurisdictions has led to increased fiscal competition among decentralized subnational governments. This study examines the relationship between fiscal competition and long-term investment in innovation at the local government level. Panel data analysis, encompassing expenditures, taxes, and innovation inputs from 18 municipalities over a 10-year period, is employed using fixed effects regression. The results reveal a negative correlation between fiscal competition and expenditure on innovation, indicating that intensified competition for mobile capital diverts resources away from essential long-term investments crucial for knowledge-driven growth. Even after controlling for economic and institutional factors, a one standard deviation increase in competition corresponds to an average decline of 25% in per capita innovation investment. These findings highlight the unintended trade-off resulting from heightened competition and underscore the need for policy frameworks that promote localized flexibility while curbing uncoordinated competition that undermines innovation capacity. While fiscal decentralization aims to foster competitive governance, this study provides empirical evidence that short-term expenditure incentives often displace long-term innovation objectives without sufficient coordination. The insights contribute significant empirical evidence on the concealed costs of fiscal competition for regional development. Consequently, a re-evaluation of conventional perspectives on decentralization and competition is warranted, emphasizing the importance of developing cooperative policy solutions that strike a delicate balance between decentralized decision autonomy and strategic coordination. Adopting such an approach is essential to fully leverage the advantages of competitive governance while simultaneously nurturing innovation ecosystems.

Dendrocalamus farinosus bamboo shoots, a species with rich nutritional value, are important in Southwest China. Lignin is an important factor affecting the postharvest flavor quality of bamboo shoots; however, the underlying mechanism of lignin deposition in D. farinosus bamboo shoots during cold storage is still not fully understood. In this study, the mutant D. farinosus XK4 with low lignin content at 3.11% and the cultivated variety ZPX at 4.47% were used as experimental materials. The lignin content of D. farinosus XK4 and ZPX, as well as the gene expression differences between them, were compared and analyzed during cold storage using transcriptomic and physiological methods. Our analysis revealed several key genes and found that D. farinosus CCoAOMT1 plays a key role in the regulatory network of bamboo shoots during cold storage. Tobacco heterologous transformation experiments demonstrated that overexpression of DfCCoAOMT1 significantly increases lignin content. This study provides a novel foundation for future research aimed at improving the postharvest quality and flavor of D. farinosus bamboo shoots through targeted genetic manipulation during cold storage. [ABSTRACT FROM AUTHOR]

In this study, the prediction of n-value (index-value) surfaces—a key indicator of the field and temperature dependence of critical current density in superconductors—across various high-temperature superconducting materials is addressed using a deep learning modeling approach. As superconductors play a crucial role in advanced technological applications in aerospace and fusion energy sectors, improving their performance model is essential for both practical and academic research purposes. The feed-forward deep learning network technique is employed for the predictive modeling of n-value surfaces, utilizing a comprehensive dataset that includes experimental data on material properties and operational conditions affecting superconductors' behavior. The model demonstrates enhanced accuracy in predicting n-value surfaces when compared to traditional regression methods by a 99.62% goodness of fit to the experimental data for unseen data points. In this paper, we have demonstrated both the interpolation and extrapolation capabilities of our proposed DFFNN technique. This research advances intelligent modeling in the field of superconductivity and provides a foundation for further exploration into deep learning predictive models for different superconducting devices. [ABSTRACT FROM AUTHOR]