1,652 research outputs found
Chemical evolution of ultra-faint dwarf galaxies in the self-consistently calculated IGIMF theory
The galaxy-wide stellar initial mass function (gwIMF) of a galaxy in
dependence of its metallicity and star formation rate (SFR) can be calculated
by the integrated galactic IMF (IGIMF) theory. Lacchin et al. (2019) apply the
IGIMF theory for the first time to study the chemical evolution of the
ultra-faint dwarf (UFD) satellite galaxies and failed to reproduce the data.
Here, we find that the IGIMF theory is naturally consistent with the data. We
apply the time-evolving gwIMF calculated at each timestep. The number of type
Ia supernova explosions per unit stellar mass formed is renormalised according
to the gwIMF. The chemical evolution of Bo\"otes I, one of the best observed
UFD, is calculated. Our calculation suggests a mildly bottom-light and
top-light gwIMF for Bo\"otes I, and that this UFD has the same gas-consumption
timescale as other dwarfs but was quenched about 0.1 Gyr after formation, being
consistent with independent estimations and similar to Dragonfly 44. The
recovered best fitting input parameters in this work are not covered in the
work of Lacchin et al. (2019), creating the discrepancy between our
conclusions. In addition, a detailed discussion of uncertainties is presented
addressing how the results of chemical evolution models depend on applied
assumptions. This study demonstrates the power of the IGIMF theory in
understanding the star-formation in extreme environments and shows that UDFs
are a promising pathway to constrain the variation of the low-mass stellar IMF.Comment: 17 pages, 16 figures, accepted for publication in A&
Reinsurance Contracting with Adverse Selection and Moral Hazard: Theory and Evidence
This dissertation includes two essays on adverse selection and moral hazard problems in reinsurance markets. The first essay builds a competitive principal-agent model that considers adverse selection and moral hazard jointly, and characterizes graphically various forms of separating Nash equilibria. In the second essay, we use panel data on U.S. property liability reinsurance for the period 1995-2000 to test for the existence of adverse selection and moral hazard. We find that (1) adverse selection is present in private passenger auto liability reinsurance market and homeowners reinsurance market, but not in product liability reinsurance market; (2) residual moral hazard does not exist in all the three largest lines of reinsurance, but is present in overall reinsurance markets; and (3) moral hazard is present in the product liability reinsurance market, but not in the other two lines of reinsurance
A survey of sustainable development of intelligent transportation system based on urban travel demand
This paper provides a comprehensive exploration of urban travel demand forecasting and its implications for intelligent transportation systems, emphasizing the crucial role of intelligent transportation systems in promoting sustainable urban development. With the increasing challenges posed by traffic congestion, environmental pollution, and diverse travel needs, accurate prediction of urban travel demand becomes essential for optimizing transportation systems, fostering sustainable travel methods, and creating opportunities for business development. However, achieving this goal involves overcoming challenges such as data collection and processing, privacy protection, and information security. To address these challenges, the paper proposes a set of strategic measures, including advancing intelligent transportation technology, integrating intelligent transportation systems with urban planning, enforcing policy guidance and market supervision, promoting sustainable travel methods, and adopting intelligent transportation technology and green energy solutions. Additionally, the study highlights the role of intelligent transportation systems in mitigating traffic congestion and environmental impact through intelligent road condition monitoring, prediction, and traffic optimization. Looking ahead, the paper foresees an increasingly pivotal role for intelligent transportation systems in the future, leveraging advancements in deep learning and information technology to more accurately collect and analyze urban travel-related data for better predictive modeling. By combining data analysis, public transportation promotion, shared travel modes, intelligent transportation technology, and green energy adoption, cities can build more efficient, environmentally friendly transportation systems, enhancing residents’ travel experiences while reducing congestion and pollution to promote sustainable urban development. Furthermore, the study anticipates that intelligent transportation systems will be intricately integrated with urban public services and management, facilitating efficient and coordinated urban functions. Ultimately, the paper envisions intelligent transportation systems playing a vital role in supporting urban traffic management and enhancing the overall well-being of urban construction and residents’ lives. In conclusion, this research not only enhances our understanding of urban travel demand forecasting and the evolving landscape of intelligent transportation systems but also provides valuable insights for future research and practical applications in related fields. The study encourages greater attention and investment from scholars and practitioners in the research and practice of intelligent transportation systems to collectively advance the progress of urban transportation and sustainable development
SGNet: Structure Guided Network via Gradient-Frequency Awareness for Depth Map Super-Resolution
Depth super-resolution (DSR) aims to restore high-resolution (HR) depth from
low-resolution (LR) one, where RGB image is often used to promote this task.
Recent image guided DSR approaches mainly focus on spatial domain to rebuild
depth structure. However, since the structure of LR depth is usually blurry,
only considering spatial domain is not very sufficient to acquire satisfactory
results. In this paper, we propose structure guided network (SGNet), a method
that pays more attention to gradient and frequency domains, both of which have
the inherent ability to capture high-frequency structure. Specifically, we
first introduce the gradient calibration module (GCM), which employs the
accurate gradient prior of RGB to sharpen the LR depth structure. Then we
present the Frequency Awareness Module (FAM) that recursively conducts multiple
spectrum differencing blocks (SDB), each of which propagates the precise
high-frequency components of RGB into the LR depth. Extensive experimental
results on both real and synthetic datasets demonstrate the superiority of our
SGNet, reaching the state-of-the-art. Codes and pre-trained models are
available at https://github.com/yanzq95/SGNet.Comment: Accepted to AAAI 202
Sensitivity Analysis for Iceberg Geometry Shape in Ship-Iceberg Collision in View of Different Material Models
The increasing marine activities in Arctic area have brought growing interest in ship-iceberg collision study. The purpose of this paper is to study the iceberg geometry shape effect on the collision process. In order to estimate the sensitivity parameter, five different geometry iceberg models and two iceberg material models are adopted in the analysis. The FEM numerical simulation is used to predict the scenario and the related responses. The simulation results including energy dissipation and impact force are investigated and compared. It is shown that the collision process and energy dissipation are more sensitive to iceberg local shape than other factors when the elastic-plastic iceberg material model is applied. The blunt iceberg models act rigidly while the sharp ones crush easily during the simulation process. With respect to the crushable foam iceberg material model, the iceberg geometry has relatively small influence on the collision process. The spherical iceberg model shows the most rigidity for both iceberg material models and should be paid the most attention for ice-resist design for ships
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