963 research outputs found
Global well-posedness of -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 with only horizontal dissipation. More
precisely, given initial data u_0=(u_0^\h,u_0^3)\in \cB^{0,\f12}, 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
EVKG: An Interlinked and Interoperable Electric Vehicle Knowledge Graph for Smart Transportation System
Over the past decade, the electric vehicle industry has experienced
unprecedented growth and diversification, resulting in a complex ecosystem. To
effectively manage this multifaceted field, we present an EV-centric knowledge
graph (EVKG) as a comprehensive, cross-domain, extensible, and open geospatial
knowledge management system. The EVKG encapsulates essential EV-related
knowledge, including EV adoption, electric vehicle supply equipment, and
electricity transmission network, to support decision-making related to EV
technology development, infrastructure planning, and policy-making by providing
timely and accurate information and analysis. To enrich and contextualize the
EVKG, we integrate the developed EV-relevant ontology modules from existing
well-known knowledge graphs and ontologies. This integration enables
interoperability with other knowledge graphs in the Linked Data Open Cloud,
enhancing the EVKG's value as a knowledge hub for EV decision-making. Using six
competency questions, we demonstrate how the EVKG can be used to answer various
types of EV-related questions, providing critical insights into the EV
ecosystem. Our EVKG provides an efficient and effective approach for managing
the complex and diverse EV industry. By consolidating critical EV-related
knowledge into a single, easily accessible resource, the EVKG supports
decision-makers in making informed choices about EV technology development,
infrastructure planning, and policy-making. As a flexible and extensible
platform, the EVKG is capable of accommodating a wide range of data sources,
enabling it to evolve alongside the rapidly changing EV landscape
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