Flow, sense and divide
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Urban-rural digitalization evolves from divide to inclusion: empirical evidence from China
Global digitalization leads to a new digital inequality, jointly faced by urban and rural areas. Diagnosing these digital challenges and seeking common coping strategies are crucial in the digital era. Yet, the state of Urban-Rural Digitalization (URD) remains ambiguous, and effective solutions are still lacking. Here we propose an integrative approach and introduce a novel Development-Gap-Integration framework to assess URD. Utilizing authoritative data from official government sources and institutes, we conduct a comprehensive analysis of 30 provinces in China from 2000 to 2020. Our findings depict a transformative journey of URD, evolving from divide to inclusion. The increasing integration accompanies by advancing development and diminishing gaps. However, we identify three significant challenges: 1) high development coinciding with high gaps, 2) inadequate integration of digital applications, and 3) widening disparity between provinces. Thus, we suggest tailored policy recommendations focused on urban-rural integration, enhancing digital literacy, and promoting regional coordinated development.
A kinematic analysis of extratropical cyclones, warm conveyor belts and atmospheric rivers
Mid-latitude weather systems play a significant role in causing floods, wind damage, and related societal impacts. Advances in numerical modeling and observational methods have led to the development of numerous conceptual models in mid-latitude synoptic and dynamical research. As these models proliferate, integrating new insights into a cohesive understanding can be challenging. This paper uses a kinematic perspective to interpret mid-latitude research in a way that synthesises various concepts and create a schematic diagram of an atmospheric river lifecycle. Our analysis demonstrates that, despite varying methods, definitions, and terminology used to describe extratropical cyclones, warm conveyor belt airflows, and atmospheric rivers, the underlying mechanisms driving their formation and development are consistent. Thus, while studying these features independently is valuable, it is important to recognise that they are all part of a larger atmospheric flow pattern. We hope this kinematic approach will serve as a bridge to link research on these phenomena.
Phosphorylation of endothelial histone H3.3 serine 31 by PKN1 links flow-induced signaling to proatherogenic gene expression
Atherosclerotic lesions develop preferentially in arterial regions exposed to disturbed blood flow, where endothelial cells acquire an inflammatory phenotype. How disturbed flow induces endothelial cell inflammation is incompletely understood. Here we show that histone H3.3 phosphorylation at serine 31 (H3.3S31) regulates disturbed-flow-induced endothelial inflammation by allowing rapid induction of FOS and FOSB, required for inflammatory gene expression. We identified protein kinase N1 (PKN1) as the kinase responsible for disturbed-flow-induced H3.3S31 phosphorylation. Disturbed flow activates PKN1 in an integrin α5β1-dependent manner and induces its translocation into the nucleus, and PKN1 is also involved in the phosphorylation of the AP-1 transcription factor JUN. Mice with endothelium-specific PKN1 loss or endothelial expression of S31 phosphorylation-deficient H.3.3 mutants show reduced endothelial inflammation and disturbed-flow-induced vascular remodeling in vitro and in vivo. Together, we identified a pathway whereby disturbed flow through PKN1-mediated histone phosphorylation and FOS/FOSB induction promotes inflammatory gene expression and vascular inflammation.
An artificial market model for the forex market
As financial markets have transitioned toward electronic trading, there has been a corresponding increase in the number of algorithmic strategies and degree of transaction frequency. This move to high-frequency trading at the millisecond level, propelled by algorithmic strategies, has brought to the forefront short-term market reactions, like market impact, which were previously negligible in low-frequency trading scenarios. Such evolution necessitates a new framework for analyzing and developing algorithmic strategies in these rapidly evolving markets. Employing artificial markets stands out as a solution to this problem. This study aims to construct an artificial foreign exchange market referencing market microstructure theory, without relying on the assumption of information or technical traders. Furthermore, it endeavors to validate the model by replicating stylized facts, such as fat tails, which exhibit a higher degree of kurtosis in the return distribution than that predicted by normal distribution models. The validated artificial market model will be used to simulate market dynamics and algorithm strategies; its generated rates could also be applied to pricing and risk management for currency options and other foreign exchange derivatives. Moreover, this work explores the importance of order flow and the underlying factors of stylized facts within the artificial market model.
Predicting debris flow pathways using volume-based thresholds for effective risk assessment
Investigating the preferential flow path of a debris flow is crucial for quantifying the risk and developing mitigation strategies. Here, we examined 66 debris flows from the Western Ghats in India employing Rapid Mass Movement Simulation (RAMMS)::Debris Flow software to understand the kinematics of run-out. Our analysis revealed that the debris flow run-out in the study area follow two main routes: 60 along the existing stream channels (SC) and six following the steepest hill slope (SH). We further simulated these debris flows to identify their drivers, and derived a threshold that distinguishes between SC and SH-type debris flows. Our results indicate that the debris flow volumes greater than 7072 cu. m is SH-type, whereas those with smaller volumes are more likely to follow SC paths. The model’s accuracy was validated against field observations, achieving a success rate of 93% for SH-type flows and 85% for SC.
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