Global well-posedness of 33-D anisotropic Navier-Stokes system with small unidirectional derivative

In \cite{LZ4}, the authors proved that as long as the one-directional derivative of the initial velocity is sufficiently small in some scaling invariant spaces, then the classical Navier-Stokes system has a global unique solution. The goal of this paper is to extend this type of result to the 3-D anisotropic Navier-Stokes system $(ANS)$ with only horizontal dissipation. More precisely, given initial data u_0=(u_0^\h,u_0^3)\in \cB^{0,\f12}, $(ANS)$ has a unique global solution provided that |D_\h|^{-1}\pa_3u_0 is sufficiently small in the scaling invariant space $\cB^{0,\f12}.

Risk and Return of Blockchain Announcements in Chinese Stock Market – An Event Study

Prior research has demonstrated that blockchain announcements are associated with significant stock market reactions on the day of the announcement. However, it is unclear what factors may influence the positive market reaction at the firm level. Moreover, it is unclear whether national policies will affect positive market reactions. Using an event study methodology, we examine investors’ reactions to blockchain announcements issued by Chinese listed companies, taking organizational factors and national policies into account. Results indicate that the stock market reacts positively to blockchain announcements in the IT sector on the day of the announcement. However, there are no significant differences between manufacturing companies and other companies regarding abnormal stock returns. In addition, a CIO (or CTO) and a high percentage of executives with a background in R&D will enhance the positive stock market reaction. Furthermore, we demonstrate that national policies play a significant role in influencing positive stock market reactions

Enabling CMF Estimation in Data-Constrained Scenarios: A Semantic-Encoding Knowledge Mining Model

Precise estimation of Crash Modification Factors (CMFs) is central to evaluating the effectiveness of various road safety treatments and prioritizing infrastructure investment accordingly. While customized study for each countermeasure scenario is desired, the conventional CMF estimation approaches rely heavily on the availability of crash data at given sites. This not only makes the estimation costly, but the results are also less transferable, since the intrinsic similarities between different safety countermeasure scenarios are not fully explored. Aiming to fill this gap, this study introduces a novel knowledge-mining framework for CMF prediction. This framework delves into the connections of existing countermeasures and reduces the reliance of CMF estimation on crash data availability and manual data collection. Specifically, it draws inspiration from human comprehension processes and introduces advanced Natural Language Processing (NLP) techniques to extract intricate variations and patterns from existing CMF knowledge. It effectively encodes unstructured countermeasure scenarios into machine-readable representations and models the complex relationships between scenarios and CMF values. This new data-driven framework provides a cost-effective and adaptable solution that complements the case-specific approaches for CMF estimation, which is particularly beneficial when availability of crash data or time imposes constraints. Experimental validation using real-world CMF Clearinghouse data demonstrates the effectiveness of this new approach, which shows significant accuracy improvements compared to baseline methods. This approach provides insights into new possibilities of harnessing accumulated transportation knowledge in various applications.Comment: 39 pages, 9 figure